FRMDB: Face Recognition Using Multiple Points of View

Contardo, Paolo; Sernani, Paolo; Tomassini, Selene; Falcionelli, Nicola; Martarelli, Milena; Castellini, Paolo; Dragoni, Aldo Franco

doi:10.3390/s23041939

Cited by 6 publications

(4 citation statements)

References 66 publications

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“…To raise an alarm that violence detection is occurring, a real-time graph was plotted with the level of violence obtained from the algorithm output and, above a certain value, the scene was identified as violent. Contardo et al [54] used a CNN pre-trained with ImageNet called MobileNetV2, which extracted spatial features and whose output was fed into two LSTMs to test which one obtained better results: a temporal Bi-LSTM and a temporal ConvLSTM.…”

Section: Cnn + Lstmmentioning

confidence: 99%

Literature Review of Deep-Learning-Based Detection of Violence in Video

Negre,

Alonso,

González-Briones

et al. 2024

Sensors

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Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant.

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Section: Cnn + Lstmmentioning

confidence: 99%

Literature Review of Deep-Learning-Based Detection of Violence in Video

Negre,

Alonso,

González-Briones

et al. 2024

Sensors

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“…Contardo et al [19] introduced the Face Recognition from the Mugshots Database (FRMDB), containing 28 mugshots and five surveillance footage of 39 individuals. The primary objective of the FRMDB is to explore the impact of different mugshot angles on the accuracy of facial recognition from surveillance video frames.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Privacy Protection in Online Federated Learning: A Method for Secure Face Image De-Identification Using a Modified Diffie-Hellman Algorithm

Thatha,

Mantena,

LingaReddy

et al. 2023

MMEP

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The proliferation of face images, alongside their widespread dissemination and easy accessibility through social media, underscores a pressing challenge to personal identification information protection. Conversely, advancements in identity-agnostic computer vision technologies offer valuable benefits, necessitating cautious utilization of face images to safeguard individual privacy. 'Face de-identification', or 'face anonymization', refers to the process of altering an original face image to a nearidentical one that obscures the subject's actual identity. Existing de-identification strategies, despite considerable efforts, often fall short in photo-realism or fail to strike an optimal balance between privacy and utility. This study proposes an approach for generating de-identified facial images using instances, addressing the potential privacy breaches and identity exposure associated with facial features. The proposed system involves a two-stage training process. Initially, a federated learning framework is suggested, enabling knowledge amalgamation through the mutual exchange of model parameters among clients during federated training, devoid of data sharing. Subsequently, sensitive information is secured using an enhanced version of the Diffie-Hellman algorithm coupled with a genetic algorithm. In the event of data loss or corruption, an optimized genetic algorithm (OGA) is employed to successfully restore the data, thereby offering protection against potential insider threats in federated learning. The decryption process is then executed as if the user had initiated the request. Experimental results demonstrate that the proposed federated learning approach delivers performance equivalent to centralized learning, thereby validating the practicality and effectiveness of the suggested architecture. Specifically, a model of the federated learning-deep convolutional neural network (FL-DCNN) achieved an accuracy of 95.2%, precision and F1-score of 95%, recall of 96%, and a final specificity of 96.80%.

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“…These systems employ biometric or behavioral characteristics to identify or verify individuals, offering significant convenience. The commonly used biometric features encompass fingerprints [1], face [2], iris [3], voice [4], and electroencephalogram (EEG) [5].…”

Section: Introductionmentioning

confidence: 99%

“…Existing studies in ECG identity recognition encounter several challenges, including: (1) Some studies struggle to effectively extract the crucial features present in the ECG signal, hampering the model's ability to discriminate between different individuals. (2) The problem of differences in feature distribution among ECG signals from various sessions has hindered many studies from achieving satisfactory results in multi-session recognition. To address the aforementioned limitations, this study makes the following contributions:…”

Section: Introductionmentioning

confidence: 99%

ECG Biometrics Based on Attention Enhanced Domain Adaptive Feature Fusion Network

Yi,

Si,

Fan

et al. 2024

IEEE Access

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In recent years, there has been an increasing focus on privacy and security among individuals. Biometric systems are highly regarded for their strong resistance to counterfeiting. Among various biometric features, ECG signals are difficult to falsify and are less attack-prone. However, as the time interval between signal acquisitions increases, the dissimilarities between individual ECG signals become more pronounced, making it difficult for many studies to achieve satisfactory recognition results in multi-session recognition. Furthermore, some studies encountered challenges in extracting crucial features from the ECG signal, which posed difficulties for identification experiments. To address the above challenge, this study proposes a novel attention-enhanced domain adaptive feature fusion network. Firstly, the network employs a multi-branch architecture to extract essential features from various dimensions of the ECG signal. Secondly, it incorporates the proposed weight fusion adaptive attention mechanism to further emphasize the features of heartbeats that contribute to recognition. Additionally, domain adaptive technology is employed to mitigate differences in feature distribution of ECG signals across sessions, thereby enhancing the model's generalization capability. Finally, four well-known databases, the ECG-ID database, PTB database, CYBHi database, and Heartprint database, were utilized to evaluate the performance of the model. These databases predominantly comprise individuals with multiple records, fulfilling the prerequisites for multi-session recognition. The model achieved recognition results of 96.31%, 73.79%, 77.80%, and 54.78% on these four databases, respectively, surpassing related studies in multisession recognition scenarios.INDEX TERMS Electrocardiogram, biometrics, multi-branch architecture, weight fusion adaptive attention mechanism, domain adaptation.

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FRMDB: Face Recognition Using Multiple Points of View

Cited by 6 publications

References 66 publications

Literature Review of Deep-Learning-Based Detection of Violence in Video

Literature Review of Deep-Learning-Based Detection of Violence in Video

Enhancing Privacy Protection in Online Federated Learning: A Method for Secure Face Image De-Identification Using a Modified Diffie-Hellman Algorithm

ECG Biometrics Based on Attention Enhanced Domain Adaptive Feature Fusion Network

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