Abstract
This paper’s main contribution presents designing and implementing a face recognition system for the android mobile phone platform from the live mobile camera. The design methodology includes two main steps. The first step is the extraction of the face features, and the second one is the recognition according to the classification of patterns. The face detection is carried out using a face detector available in the Android called feature-based method machine learning (ML) Kit Software Development Kit (SDK). The face features include nose detection, mouth detection, eyes detection, and cheek detection. These features are detected based on their geometrical dimensions. Twenty-five geometrical raw distances are reduced to 23 normalized distances by referring them to their face width and height. The recognition step is done by computing the correlation coefficient ratio between the test image’s normalized distance and all normalized distances for authorized persons stored in the training database. The system will allow access to the mobile applications if the correlation coefficient is greater than a chosen threshold; otherwise, it rejects the person. The proposed approach’s recognition rate has achieved an accuracy more than 95% for appropriate chosen threshold. The time taken to recognize a face is approximately 13 s of other approaches.
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Rahouma, K.H., Mahfouz, A.Z. (2021). Applying Mobile Intelligent API Vision Kit and Normalized Features for Face Recognition Using Live Cameras. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_38
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DOI: https://doi.org/10.1007/978-3-030-76346-6_38
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