Abstract
Face recognition is a difficult task in the realm of computer vision and image analysis. A face recognition model can recognize a face in an image automatically. When the face is orientated in different angles, most of the face recognition systems fail to identify the face, resulting in the degradation of the performance of the facial recognition system. To handle this issue, dataset containing face images should be created in such a manner that it should contain the facial images at varying angles. Face recognition systems based on convolutional neural networks (CNNs) have gained popularity because of its effectiveness in delivering authenticity by automatically identifying faces. The proposed system consists of a robust CNN model for detecting and recognizing the face at varying angles. The proposed CNN model is tested on three datasets, namely ‘MepcoECE’ dataset which was created with 24 subjects in real-time which consists of facial images at varying angles, and two benchmark datasets, namely ORL dataset and GTF dataset, and achieved a superior performance in benchmark datasets.
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Ahila Priyadharshini, R., Hariharan, S., Jagadeeswara, R. (2023). A CNN-Based Approach for Face Recognition Under Different Orientations. In: Das, A.K., Nayak, J., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. CIPR 2022. Lecture Notes in Networks and Systems, vol 725. Springer, Singapore. https://doi.org/10.1007/978-981-99-3734-9_14
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