Face Recognition – Extensive Survey and Recommendations

Razzaq, Ali Nadhim; Ghazali, Rozaida; Abbadi, Nidhal K. El

doi:10.1109/icoten52080.2021.9493444

Cited by 3 publications

(2 citation statements)

References 62 publications

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“…The use of facial recognition systems in differing domains such as surveillance, airports, and personal devices is well-established. These systems have proven to be highly effective and accurate in verifying the identity of subjects (Razzaq et al, 2021;Wang and Deng, 2021). However, as facial recognition becomes increasingly integrated into our daily lives, it is crucial to consider the potential for biases and discrimination against certain demographic groups.…”

Section: Introductionmentioning

confidence: 99%

Child face recognition at scale: synthetic data generation and performance benchmark

Falkenberg,

Bensen Ottsen,

Ibsen

et al. 2024

Front. Signal Process.

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We address the need for a large-scale database of children’s faces by using generative adversarial networks (GANs) and face-age progression (FAP) models to synthesize a realistic dataset referred to as “HDA-SynChildFaces”. Hence, we proposed a processing pipeline that initially utilizes StyleGAN3 to sample adult subjects, which is subsequently progressed to children of varying ages using InterFaceGAN. Intra-subject variations, such as facial expression and pose, are created by further manipulating the subjects in their latent space. Additionally, this pipeline allows the even distribution of the races of subjects, allowing the generation of a balanced and fair dataset with respect to race distribution. The resulting HDA-SynChildFaces consists of 1,652 subjects and 188,328 images, each subject being present at various ages and with many different intra-subject variations. We then evaluated the performance of various facial recognition systems on the generated database and compared the results of adults and children at different ages. The study reveals that children consistently perform worse than adults on all tested systems and that the degradation in performance is proportional to age. Additionally, our study uncovers some biases in the recognition systems, with Asian and black subjects and females performing worse than white and Latino-Hispanic subjects and males.

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Section: Introductionmentioning

confidence: 99%

Child face recognition at scale: synthetic data generation and performance benchmark

Falkenberg,

Bensen Ottsen,

Ibsen

et al. 2024

Front. Signal Process.

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“…For this project, "who it is" was used with the help of biometrics in the area of face and voice recognition, which are unique personal characteristics and parameters [19], [20]. In order to strengthen the authentication system, Deep Learning techniques were applied [21], [22] in the fields of artificial vision, natural language processing, and speech processing [19]. The research was carried out in the southern Colombian city of Popayan.…”

Section: Introductionmentioning

confidence: 99%

Access Control System based on Voice and Facial Recognition Using Artificial Intelligence

Capote-Leiva¹,

Villota-Rivillas²,

Muñoz³

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Computer security has become a matter of great concern at the global level. Whatever the economic sector, all companies handle confidential information related to clients and personnel. These latter therefore need to be seen as sensitive assets that require protection. Appropriate, consensual handling of personal information is thus a legal and delicate requirement that demands to be treated securely in all types of business. Many companies and clients have lost sums of money in the millions due to incorrect information protection, triggering complicated, expensive proceedings that are awkward and cumbersome to resolve. To implement an authentication system based on biometric parameters, which strengthens the security of sensitive assets in areas considered critical in various organizations by attaining the highest accuracy in user classification processes. By applying a convolutional neural network: MobileNet, viewing via computer and low-cost devices (Raspberry Pi 3). The system constitutes an authentication device for voice and face with 96% and 100% accuracy, respectively. Conclusion: The system shows that deep learning (Deep Convolutional Neural Networks), in combination with devices such as the Raspberry, generate a system capable of high performance in time and cost, as well as providing companies with a robust system featuring high accuracy in the correct recognition of the biometric patterns of users registered and trained by the system.

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