Abstract
Face verification systems have many challenges to address because human images are obtained in extensively variable conditions and in unconstrained environments. Problem occurs when capturing the human face in low light conditions, at low resolution, when occlusions are present, and even different orientations. This paper proposes a face verification system that combines the convolutional neural network and max-margin object detection called MMOD + CNN, for robust face detection and a residual network with 50 layers called ResNet-50 architecture to extract the deep feature from face images. First, we experimented with the face detection method on two face databases, LFW and BioID, to detect human faces from an unconstrained environment. We obtained face detection accuracy > 99.5% on the LFW and BioID databases. For deep feature extraction, we used the ResNet-50 architecture to extract 2,048 deep features from the human face. Second, we compared the query face image with the face images from the database using the cosine similarity function. Only similarity values higher than 0.85 were considered. Finally, the top-1 accuracy was used to evaluate the face verification. We achieved an accuracy of 100% and 99.46% on IMM frontal face and IMM face databases, respectively.
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This work has been funded by the Faculty of Informatics, Mahasarakham University, Thailand [grant number it2-05/2560].
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Khamket, T., Surinta, O. (2021). Feature Extraction Efficient for Face Verification Based on Residual Network Architecture. In: Chomphuwiset, P., Kim, J., Pawara, P. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2021. Lecture Notes in Computer Science(), vol 12832. Springer, Cham. https://doi.org/10.1007/978-3-030-80253-0_7
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