Street Soldiers: Facial recognition In this week's episode of Street Soldiers, FOX 5 NY's Lisa Evers take a closer look at today's facials recognition technology with three experts: Jon Padfield, host of the Business Reform YouTube Channel, criminal defense attorney Phillip Hamilton and Ralph Cilento, co-founder of Facial Recognition Investigative Consultants.
Police will deploy live facial recognition at a multi-arts festival in Lowestoft. The technology will be used at the First Light Festival on Saturday, June 20, to identify individuals wanted in connection with serious offences. Suffolk police describe the move as part of broader efforts to embrace innovative tools that support investigations and prioritise public safety. Chief Inspector Andy Pursehouse said: "This innovative technology can play a vital role in protecting people, keeping them safe, investigating crimes and getting justice for victims. "It also means we are saving time and effort for our officers, leaving them able to do other work to protect and help the public. "The presence of the van is also a proven and powerful engagement tool." The forces says that last year more than 200 people came to speak to officers on the ground at the festival to understand more about the technology. Last year’s deployment also led to three arrests and engagement. Chief Inspector Pursehouse added: "I’m confident we will see a similar amount of interest this year as well. "Once again, we will be able to show the public how the system operates and show how the data is deleted and faces are blurred in less than a second if they are not matched against the watchlist. "It’s essential to say those attending the festival should feel reassured that we are only looking for those individuals who have committed serious offences." Live facial recognition compares faces captured on live camera feeds against a pre-determined ‘watch list’ of individuals of interest to police. Images not matching the list are deleted instantly and not stored but if a match is found, officers on the ground will verify the person’s identity and determine the appropriate next steps.
Man sues law enforcement alleging AI facial recognition technology led to wrongful arrest A Florida man is suing multiple law enforcement agencies after he alleges he was wrongfully arrested due to flawed AI facial recognition technology. "I don't wish this on my worst enemy," Robert Dillon, a father of one, told ABC News. Police body camera video footage, obtained by ABC News affiliate Gulf Coast News, shows Dillon's 2024 arrest outside his San Carlos Park home. "I was basically telling them this is crazy. I have no idea who did this, but it's not me," Dillon recalled of the arrest. Authorities had been searching for a suspect who allegedly tried to lure a child at a Jacksonville Beach restaurant, more than 300 miles away from Dillon's home. "To be accused of a heinous crime of that nature, I'm thinking I may not be coming back home," Dillon said. During their investigation, authorities allegedly fed "poor quality" surveillance images of the suspect into an AI-powered facial recognition program, which scanned facial features and found photos of the suspect and Dillon were allegedly "a 93% match," according to Dillon's lawsuit. Dillon said he knew he had been incorrectly identified, because he had never been to Jacksonville Beach. "I argued with the police officer for 20 minutes, and he insisted that 'I know that I'm looking at your mugshot,'" he said. The state attorney's office dropped Dillon's case weeks after his arrest, but Dillon said it took nearly a year to get the arrest wiped from his record, with help from the American Civil Liberties Union. Dillon and the ACLU are currently suing several law enforcement authorities and agencies, including the Pinellas County Sheriff's Office, which oversees the use of the AI facial recognition technology across Florida. "Despite [a] well-documented history of [facial
Erica Smith, CUSD Director of Communications and Community Relations, said that the camera installation schedule remains undecided, but said work will likely take place over summer to minimize the effect on campus activities. The cameras, made by tech company Verkada, use features like AI-powered facial recognition, license plate identification and text-based searching. Data is stored for 30 days, according to Verkada’s website. Data collected by the cameras belongs solely to the school district, unless the district gives explicit access to an outside party. The cameras are capable of sharing live footage with local law enforcement in emergency situations. The school board unanimously approved the camera replacements at a May 20 board meeting to only enable the camera’s vehicle history feature while disabling facial recognition features. However, the board didn’t discuss its ability to eventually enable the contentious facial recognition functions. Parents and students repeatedly expressed their concerns over privacy throughout CUSD board meetings, bringing student safety into question. Many cited Verkata’s numerous data leaks throughout the company’s history as a red flag. In 2021, the Federal Trade Commission sued the company for nearly $3 million after it failed to secure customer data, which led to a hacker accessing private customer cameras, according to the FTC website. Verkada is now annually audited by the FTC to ensure that security measures are continually updated and secure. REASONS FOR REPLACEMENT At the Feb. 18 board meeting, CUSD Director of Information Technology John Vincent spoke on why CUSD’s current cameras should be replaced — primarily, he said, because current systems are “antiquated” and approaching the end of its usability. After assessing different proposals, Vincent said using Verkada costs less than the current cameras used. He said that current cameras would cost $4.6 million to maintain and renew over a 10-year period, whereas Verkada will
1. Introduction Over the past decade, deep learning as a field has grown quite significantly, whether it be the compute capacity of hardware or the ingenuity behind architectures that utilize that hardware. But if you think about it for more than a second, the underlying architecture has remained consistent in a few key areas. We’ve seen a massive shift from convolutional networks to the new Transformer architectures that power today’s large language models, but the way these networks route information from one layer to another hasn’t changed all that much. Recently, researchers at DeepSeek-AI released a paper titled “mHC: Manifold-Constrained Hyper-Connections,” (Xie et al., 2025b)1 which proposes an entirely new redesign of this routing system. To really appreciate the solution they came up with, let’s look at how signal propagation has evolved over the past few generations of models, and why the current methods are hitting a wall. 2. The Backbone: Standard Residual Connections Firstly, to understand the specific problem that the authors are trying to solve, we need to talk about where it all started–The standard Residual Connection (He et al., 2015)2. Introduced back in 2015 with ResNets, the residual connection is arguably one of the most important architectural design choices used in every AI model out there. Mathematically, it looks like this: It simply means that the final output of a layer is the sum of its output and the input it originally got. The key component here is that bare xl term in the residual stream, which we call the identity mapping. It’s important because it acts as an uninterrupted pathway for the gradient signal to flow through the entire network from start to finish. This property is exactly what prevents gradients from vanishing or exploding during training and allows us to successfully train models with hundreds
Federal authorities are investigating large markings discovered in the grass on the National Mall that appear to spell out “86 47,” and the discovery quickly triggered a federal response that included evidence collection, testing and a review by multiple agencies. The markings were found Thursday near the World War II Memorial. Investigators have not determined how they were created, who made them or what message was intended. Download the SAN app today to stay up-to-date with Unbiased. Straight Facts™. Point phone camera here Investigators collect evidence Photographs taken from the Washington Monument by a Reuters photographer show what appears to be the sequence “8647” etched into the lawn. The eight is the easiest to identify, while the four and the seven are far less distinct, making the full message difficult to recognize from ground level. U.S. Park Police responded shortly before noon after receiving a vandalism report. Officers and investigators secured part of the area, collected grass samples and began examining what caused the discoloration. National Guard personnel were also seen at the site as authorities worked around several brown patches in the grass. The Interior Department characterized the incident as vandalism and said it would be investigated aggressively. “Any threat against the president is taken very seriously by the Department, and our U.S. Park Police will investigate this incident and hold those responsible accountable,” an Interior Department spokesperson said. Why the numbers matter The phrase “86 47” has become a flashpoint in political and legal disputes surrounding President Donald Trump. The number 47 refers to Trump as the nation’s 47th president. The meaning of “86” is more contested. Merriam-Webster traces the term to restaurant and service-industry slang commonly used to mean remove, reject or get rid of something. Trump allies and federal prosecutors have argued that, when paired with
U.S. Marines with the 2nd Marine Logistics Group color guard march during a change of command ceremony at Marine Corps Base Camp Lejeune, North Carolina, June 11, 2026. The change of command ceremony is a time-honored tradition which formally signifies the transfer of command and entails the total accountability, authority and responsibility from one individual to another. (U.S. Marine Corps photo by Sgt. Alfonso Livrieri) | Date Taken: | 06.11.2026 | | Date Posted: | 06.12.2026 05:11 | | Photo ID: | 9743325 | | VIRIN: | 260611-M-GD991-1093 | | Resolution: | 7593x5062 | | Size: | 6.46 MB | | Location: | MARINE CORPS BASE CAMP LEJEUNE, NORTH CAROLINA, US | | Web Views: | 24 | | Downloads: | 1 | This work, 2nd Marine Logistics Group Change of Command Ceremony [Image 12 of 12], by Sgt Alfonso Livrieri, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
Abstract Multilevel image thresholding is an important segmentation technique that partitions an image into meaningful regions in applications such as object recognition, medical imaging, and satellite image analysis. However, conventional techniques are limited by their poor sensitivity to initial conditions, lack of sufficient spatial contextual information, and slow convergence. The proposed method uses a novel hybrid model based on the Artificial Hummingbird Algorithm (AHA) to address the limitations of existing approaches. Here, Latin Hypercube, Sobol, Halton, and Sierpinski strategies are used during the population initialization phase to improve population diversity and search space coverage. This improves the exploration capability of the algorithm and supports better search space. The proposed methodology also uses spatial contextual information to improve the quality of segmentation. It also incorporates a relationship between neighboring pixels in order to retain more structure and enhance the visual performance of the output. For the exploitation phase, the Great Deluge Algorithm (GDA) is utilized as the optimization algorithm. Furthermore, the use of GDA serves as an adaptive acceptance function which reduces the chance of getting stuck during the search process. Minimum Cross Entropy Measure (MCEM) is used as the objective function to obtain optimal threshold values. Different evaluation metrics have been used to compare the results of the proposed method with other existing metaheuristic algorithms. The code is available at https://github.com/suprajatirumalasetti/AHA_GDA_Image_Segmentation_Code. Funding Open access funding provided by Vellore Institute of Technology- AP University. Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as
Expert Q&A: AI & Food Safety Beyond the AI Hype AI is not a detection tool. It interprets patterns in data, which makes validation, governance, and human oversight essential to its use in food safety systems, says Willette Crawford, owner and principal of Katalyst Consulting LLC. This interview is part of our extended Q&A series exploring AI and food safety. As AI adoption grows, expectations for what these systems can do are not always aligned with reality. AI can identify patterns and highlight potential risk, but it cannot confirm contamination or replace human judgment. In this Q&A, Willette Crawford, owner and principal of Katalyst Consulting LLC, explains where the technology adds value and why validation and oversight are critical. Where do you see AI generating measurable value in food plants? AI is generating the most measurable value in areas where data is continuous, structured, and directly tied to decisions such as environmental monitoring, sanitation verification, and process control. The real impact isn’t automation, it’s prioritization. AI helps teams recognize meaningful signals earlier and act before a loss of control occurs. In that way, it strengthens preventive controls by improving timing and consistency, rather than replacing the system itself. Where does the hype around AI exceed the science? The hype exceeds the science when AI is positioned as a detection tool rather than what it actually is: a pattern recognition system built on imperfect data. AI doesn’t detect pathogens; it interprets signals that may correlate with risk. Without strong data quality, context, and validation, it can create a false sense of precision. So, the danger isn’t the technology itself—it’s overconfidence in what it can actually prove. The effectiveness of AI depends heavily on the quality of the data and the context in which the system is built. The real impact isn’t
A 93% police face match in Florida sent a Fort Myers crabber to jail after a child-luring case, but the arrest is fueling a bigger fight over one of the oldest facial-recognition systems in the US Synopsis Robert Dillon was wrongfully arrested after facial recognition software flagged him for a crime committed hundreds of miles away. This incident highlights the dangers of relying on AI for law enforcement, as a grainy photo led to an arrest warrant based solely on the technology's output. The ACLU lawsuit underscores the need for caution with these error-prone tools. How one grainy photo led to a wrongful arrest Police responded to reports of an attempted child abduction at a Jacksonville Beach McDonaldâs in November 2023. One witness said a man tried to persuade a girl under the age of 12 to go away with him. According to Reason magazine, the responding officer didnât even get a copy of the security footage; he just took cell phone pictures of the surveillance screen. Weeks passed with no leads. The investigating officer compared the photos to booking records and the sex offender registry but found no matches. Eventually, he sent the photos to other agencies for help, and thatâs when an investigator ran them through facial recognition software, which flagged Dillon. A police report reportedly said the software returned the match with high confidence. From there it was all downhill. According to the ACLU's official statement, Jacksonville Beach police based their arrest warrant solely on that facial recognition hit and a statement from a restaurant employee who picked Dillon's photo from a lineup. The issue? Dillon lived hundreds of miles away. Investigators even ran a license plate reader database, the suit says, which showed no sign of Dillonâs vehicles anywhere near Jacksonville Beach in the 48 hours
Offering a variety of advertising and sponsorship options for reaching influential specialists from targeted demographic splits. Cureus provides an equitable, efficient publishing and peer reviewing experience without sacrificing publication times. Generate broad awareness and deliver relevant, peer-reviewed clinical experiences directly to potential customers. Dedicated Cranial Radiosurgery: Clinical Experience with New & Innovative SRS Technologies Sponsored by Zap Surgical Systems Real-Time Adaptive Motion Management on Helical and Robotic RT Platforms Sponsored by Accuray, Inc. Cureus Journal of Medical Science Please note that by doing so you agree to be added to our monthly email newsletter distribution list. You can unsubscribe anytime. By joining Cureus, you agree to our Privacy Policy and Terms of Use.
Abstract Otitis media (OM) and its complications can cause immense suffering for patients. However, due to the limited number of experts and their knowledge base, human specialists can only handle a limited number of CT images. Therefore, the auxiliary diagnosis of OM is crucial. Object detection is one of the primary tasks in computer vision. With the development of Transformers and attention mechanisms in recent years, the DETR series detectors have gradually become mainstream. However, these detectors are highly sensitive to the number of decoder layers, and slight deviations in the layers significantly impair model performance. The required number of decoder layers varies across different datasets, with grayscale images such as CT scans likely needing a different number of decoder layers than color images. Considering the stringent requirements for performance in clinical CT diagnostics, this paper analyzes the decline in model effectiveness due to excessive decoder layers. It proposes a new model called 4DO-DETR to address the instability issues of DETR performance. Regarding performance, 4DO-DETR has shown significant improvement over the baseline model (DN-DAB-DETR), surpassing the state-of-the-art (SOTA) algorithms Co-DETR, DINO and RT-DETR in the past two years. Rigorous experimental evaluations on a benchmark medical dataset demonstrate that our method achieves higher scores than other sophisticated network models——our model’s mAP reached 56.8%, higher than DINO’s 54.7%, Co-DETR’s 54.0%, and the baseline’s 45.1%. The datasets employed in this paper are available at https://github.com/promisedong/Four-DO-DETR. Similar content being viewed by others Introduction Otitis media (OM) is a common ear disease worldwide that can lead to hearing loss, ear pain, and, in severe cases, serious complications such as facial paralysis and intracranial infection1. Accurate and timely diagnosis is therefore crucial for effective treatment and prevention of complications. Computed tomography (CT) imaging plays an important role in OM detection, providing high-resolution three-dimensional views of the
Missing Since 1997: New Image of Sabrina Aisenberg Could Spark Answers More eyes are about to turn to one of the nation’s most baffling infant abduction cases. Later this summer, a documentary series is expected to revisit the disappearance of Sabrina Aisenberg, exploring the mystery that has captivated investigators and the public for nearly three decades. As Sabrina’s 29th birthday approaches on June 27, the National Center for Missing & Exploited Children (NCMEC) is releasing a new age-progression image showing what she may look like today. “Age-progression images can spark recognition that leads to critical new information,” said John Bischoff, vice president of NCMEC's Missing Children Division. “We hope this image of Sabrina reaches someone who recognizes her, or perhaps even Sabrina herself, and helps bring long-awaited answers to her family and the community.” Sabrina Aisenberg at 5 months old and an age-progression showing what she may look like at 28 years old. (Credit: NCMEC) Sabrina was just 5 months old when she vanished from her home in Valrico, Florida, on Nov. 24, 1997. Despite decades of investigation, no trace of her has ever been found. Here’s what we know: - According to her mother, Marlene Aisenberg, Sabrina was sound asleep in her crib when she checked on her around midnight. - Marlene and Steve Aisenberg told investigators they woke up around 6:30 a.m. and discovered Sabrina was missing. - While searching the house for their daughter, the parents reported finding the garage door and the door leading from the garage into the house open. - The parents also reported that a handmade yellow blanket was missing from Sabrina's crib. Searches for Sabrina were launched immediately, and the case drew national attention, including being featured on the TV show, “America’s Most Wanted.” “After 28 years, we still believe answers are
A Florida man has filed a lawsuit against multiple U.S. law enforcement agencies, claiming he was wrongfully arrested and falsely accused of luring a child after malfunctioning facial recognition technology misidentified him. When the 52-year-old man was arrested in August 2024 at his home in front of his wife and daughter, in San Carlos Park, Fla., outside of Fort Myers, he was informed he had been caught on McDonald’s security cameras in Jacksonville Beach allegedly attempting to lure a girl under the age of 12 into his company, according to the lawsuit. The man, who resides some 300 miles from Jacksonville Beach, where the alleged crime occurred, had never set foot in the area, the legal filing states. The case was dismissed last year, and his charges were dropped in connection with the alleged incident in November 2023, according to the American Civil Liberties Union (ACLU), which filed the case on his behalf, though residual “trauma” remains, it says. Get breaking National news The lawsuit, which has been brought against the Jacksonville Beach Police Department, the Jacksonville sheriff’s office, and Bob Gualtieri, the sheriff of Pinellas County, accuses investigators of arresting the man using “an incorrect result from facial recognition” and “concealing evidence that showed he could not have committed the crime.” The lawsuit describes a flawed photo lineup presented to a witness, in which they incorrectly identified an innocent man as the suspect due to “automated bias.” Facial recognition technology is designed to find the closest-looking face in a database. However, if the suspect is not in the database, the system can still return an innocent person who resembles them, the lawsuit explains. The candidate is then placed among several filler images selected to resemble the perpetrator, a standard practice to ensure the suspect does not stand out. But
FLORIDA — A Fort Myers man says he was wrongfully arrested for attempting to lure a child after police relied on facial recognition technology that incorrectly identified him as a suspect, leading to months of prosecution before charges were dropped. According to the lawsuit, Robert Dillon claims law enforcement agencies and officers used an artificial intelligence-powered facial recognition system to identify him as the suspect in a 2023 child luring case at a Jacksonville Beach McDonald's, despite evidence that he says would have cleared him. The lawsuit states 52-year-old Dillon had never been to Jacksonville Beach and lived more than 300 miles away in Fort Myers. He claims investigators relied on a facial recognition search that reportedly produced a "93% match" from surveillance images and failed to pursue other investigative steps that could have ruled him out as a suspect. The complaint accuses the Jacksonville Beach police of using low-quality surveillance images taken from a computer screen and submitting them through the Face Analysis Comparison and Examination System (FACES), a facial recognition database operated by the Pinellas County Sheriff's Office. According to the lawsuit, investigators later obtained a photo lineup identification from a McDonald's manager who said she recognized the suspect as a regular customer but did not witness the alleged interaction between the child and the suspect. Dillon claims the identification was tainted because it was based on the same facial recognition lead. The lawsuit also claims investigators failed to disclose several pieces of potentially exculpatory information when seeking an arrest warrant, including Dillon's statements that he had never been to Jacksonville Beach, license plate reader data that did not place his vehicles in the area and that the suspect was described as a regular customer at the restaurant. Dillon was arrested at his Fort Myers home in August
Police to deploy facial recognition cameras again A police force plans to deploy live facial recognition (LFR) technology in a city centre for a second time on 19 June. Cambridgeshire Police said cameras would be used to identify individuals who pose the greatest risk to public safety in Peterborough, although the specific location of the cameras has not been confirmed. The system scans faces from a live camera feed and compares them in real time with a watchlist of high-risk individuals. During its first use on 19 May, police scanned 34,000 faces in a six-hour operation on Bridge Street and Long Causeway, leading to the arrest of two men wanted for failing to appear in court - one accused of theft from a person and the other of shoplifting. Five other people were identified, including a woman suspected of breaching a criminal behaviour order. Police said any matches are reviewed by an officer, while images and biometric data are deleted either immediately or within 24 hours if no further action is needed. Insp Sam Tucker described the previous deployment of LFR as "a successful day", adding that officers also responded to other incidents, including a medical emergency. He said the operation received a positive response from the public and local businesses, with some reporting a reduction in crime. However, the force acknowledged people's concerns over the technology in regards to privacy. A spokesperson for the force said: "Data collected via LFR is handled according to data protection laws and regulations. "Personal information is only retained if a match is made and deemed necessary for investigation purposes. "Images of people who do not match anyone on the watchlist are immediately deleted and cannot be recovered. "All CCTV footage from mobile deployments is deleted within 31 days unless it is required for
Florida man blames wrongful arrest on “error-prone” AI facial recognition When police arrested Richard Dillon in 2023 for allegedly trying to “lure a child” away from a McDonald’s in Jacksonville Beach, Florida, he told them he was more than 300 miles away at the time of the crime. The key evidence police used to puncture his alibi: facial recognition software matched an image of the suspect to Dillon’s photo. Dillon was later cleared, and on Wednesday he became a plaintiff in a new ACLU lawsuit filed against the Jacksonville Beach Police Department and others over what he believes was a case of misuse of the AI-driven image matching technology. “Police let an error-prone artificial intelligence system stand in for an investigation,” they argue in their complaint. The case is the latest attempt to establish guardrails for powerful new technology that police are increasingly using to solve one of the toughest aspects of any investigation — when they have an image of a suspect, but not that person’s identity. Facial recognition is an increasingly common law enforcement tool, with public databases holding images of 117 million Americans, according to the Center on Privacy and Technology at Georgetown Law School. Jacksonville Beach police and the Jacksonville Sheriff’s Office both declined to comment. The episode began in November 2023, when police say a man approached a 12-year-old in a McDonald’s and tried to lure her away from her parents. A month later, Dillon received a call from Jacksonville Beach Police Officer Scott O’Connell. He says that during the call, O’Connell “accused me over and over again of a heinous crime that I knew I didn’t commit”. Dillon told CBS News he remembers thinking “my life is over. … AI says I did this, how am I going to prove that I didn’t?” During
When you buy through links on our articles, Future and its syndication partners may earn a commission. Welcome to the future, where there's always a camera lens within arm's reach. I'll park my wider-ranging rant about surveillance culture for the time being and attempt to just focus on the news story that's most recently made me consider stepping on to the soap box: the facial recognition system that was at one point in Meta's smart glasses app. Last week, Wired reported it had found code that suggested Meta had quietly embedded face-recognition technology into its smart glasses app. Now Wired reports that, since it published its original story, the code in question has been removed from the Meta AI app. That's good news, right? Well, I'm not sure I feel especially great about it. The code Wired identified was referred to internally as 'NameTag,' and reportedly added over multiple updates in 2026. It was reported that once activated, the system could identify the faces of those captured by the smart glasses' lens and that it would then alert the user when it recognised someone. The original story broke mere weeks after a Meta spokesperson told the publication, "If we were to release such a feature, we would take a very thoughtful approach before rolling anything out." Wired found this particularly troubling as code for the system had been quietly pushed out to an app downloaded millions of times. Couple this with reports that Niantic Spatial may be using images of the world captured by Pokemon Go players to build its highly-accurate geospatial model for use by delivery robots and potentially defense applications, and the whole episode leaves me feeling queasy. At the very least, it's a good time to re-assess what data you're handing over to big tech when you
No barriers for 50,000 users of new ‘seamless’ e-channels at mega bridge Facial recognition technology will allow people to clear immigration in five seconds using new e-channels at Hong Kong-Zhuhai-Macau Bridge About 50,000 regular users of the Hong Kong-Zhuhai-Macau Bridge will soon be able to pass through new immigration e-channels without stopping or displaying their identity cards, as facial recognition will be used for clearance. Or Chung-yuk, assistant director of information systems at the Immigration Department, said on Thursday that Hong Kong permanent residents aged 11 or above who were regular users of the bridge could use two new “seamless” e-channels from June 25. “Users will not need to stop or display their identity cards during the process, bringing a seamless experience to their crossing,” Or said. Residents who have arrived at or departed from Hong Kong’s clearance building at the bridge at least 10 times over the past 90 days can register to use the new e-channels starting on Thursday. Or estimated that around 50,000 local travellers, accounting for around 20 per cent of all bridge users, would be eligible. Two seamless e-channels have been installed in the Hong Kong border crossing departure hall. Unlike other existing e-channels, the new version has an open-gate design and a 4.9-metre-long walkway, with passengers not required to stop or present their identity cards when crossing.
On Brig. Gen. Tracey Michael's last day as Commanding General of the 68th Theater Medical Command he and Command Sgt. Maj. Kyle Brunell showed appreciation to several people before the Change of Command ceremony took place. These individuals have supported the 68th TMC as well as help make the Change of Command ceremony go flawless. Thank you for all your support to the 68th TMC and job well done!!!! (U.S. Army photos by Sgt. 1st Class Eric Johnson) "CONSERVE POWER" | Date Taken: | 06.08.2026 | | Date Posted: | 06.11.2026 12:58 | | Photo ID: | 9743435 | | VIRIN: | 260609-A-JW006-3584 | | Resolution: | 6240x4160 | | Size: | 4.94 MB | | Location: | SEMBACH, RHEINLAND-PFALZ, DE | | Web Views: | 3 | | Downloads: | 0 | This work, 68th TMC COC Recognition [Image 14 of 14], by SFC Eric Johnson, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.