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Efficient color face recognition based on quaternion discrete orthogonal moments neural networks

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Abstract

In recent years, with the rapid development of multimedia technologies, color face recognition has attracted more attention in various areas related to the computer vision. Extracting pertinent features from color image is a challenging problem due to the lack of efficient descriptors. Many methods in literature have been reported. However, some inconveniences arising from these methods are: insufficient color information and time consuming for features extraction. In this paper, a new model quaternion discrete orthogonal moments neural networks (QDOMNN) is proposed to improve the accuracy of color face recognition. The quaternion representation is used to represent color image in a holistic manner instead of monochromatic intensity information. Furthermore, the discrete orthogonal moments are used to extract compact and pertinent features from quaternion representation of image. The main purpose of the utilization of quaternion discrete orthogonal moments is to reduce the number of parameters in the input vector of the model, and consequently decreasing the computational time of training process, while improving the classification rate. The performance of our model is evaluated on some face databases, we obtain 100% as classification accuracy on faces94, grimace and GT, 91.93% on FEI, more than 94.72% on faces95 and more than 98.01% on faces96. Experiment results show the outperformance of our model (QDOMNN) against other existing methods in terms of classification rate, and robustness in noisy conditions.

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El Alami, A., Berrahou, N., Lakhili, Z. et al. Efficient color face recognition based on quaternion discrete orthogonal moments neural networks. Multimed Tools Appl 81, 7685–7710 (2022). https://doi.org/10.1007/s11042-021-11669-3

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