Feature Extraction and Fusion for Face Recognition Systems using Pre-Trained Convolutional Neural Networks

Almabdy, Soad; Elrefaei, Lamiaa A.

doi:10.12785/ijcds/100144

Cited by 5 publications

(1 citation statement)

References 19 publications

(26 reference statements)

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“…Experimental results on three databases, including CMU PIE, CASIA, and Poly-U, show that the proposed method improved recognition accuracy in the range of 96-97%. Likewise, Almabdy and Elrefaei [26] presented a face recognition method based on a combination of features. In the proposed method, feature extraction is performed by AlexNet and ResNet-50 convolutional neural networks, and the extracted features are combined.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction from Several Angular Faces Using a Deep Learning Based Fusion Technique for Face Recognition

Charoqdouz,

Hassanpour

2023

IJE

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Due to its non-interfering nature, face recognition has been the most suitable technology for designing biometric systems in recent years. This technology is used in various industries, such as health care, education, security, and surveillance. Facial recognition technology works best when a person is looking straight into the camera. On the contrary, the performance of facial recognition degrades when encountered with an angled facial image, because they are generally trained using images of a full face. The purpose of this paper is to estimate the feature vector of a full face image when there are several angular facial images of the same person, one example being angular faces in a video. This method extracts the basic features of a facial image using the non-negative matrix factorization (NMF) method. Then, the feature vectors are fused using a generative adversarial network (GAN) to estimate the feature vector associated with the frontal image. The experimental results on the angular images of the FERET dataset show that the proposed method can significantly improve the accuracy of facial recognition technology methods.

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Section: Introductionmentioning

confidence: 99%