Abstract
Face recognition has received significant attention because of its numerous applications in access control, security, and surveillance system. In real-world scenarios, uncontrollable lighting conditions, a variety of posture and facial expressions, and noisy facial images can degrade the face recognition accuracy. Hence, the research based on the HOG algorithm and pre-processing implementation framework processing framework to improve face recognition accuracy is proposed. This proposal consists of four stages where the first stage is to build a dataset of 15 subjects and has five series of multi-poses of facial images. The second stage is focused on enhancing the pre-processing framework that consists of denoising colored, illumination normalization, and facial alignment algorithms. For the third stage, the HOG algorithm is utilized as a feature descriptor to detect the face. The fourth stage is implementing the deep convolution neural network to evaluate the accuracy of face recognition. From the observation, the improvement in the accuracy rate is up by 4.37% after the enhancement of the pre-processing framework.
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Acknowledgements
This research collaborates between the Robotics, Computer Vision & Image Processing (RCVIP) Research Group Lab at the School of Computer Sciences, Universiti Sains Malaysia (USM) and Intel Technology Sdn. Bhd., Penang. This project was supported by USM Short Term Grant (PKOMP/6315262).
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Seng, Y.W., Wahab, M.N.A., Chuan, W.C., Wen, K.Y.K., Lun, L.T. (2022). Enhanced the Face Recognition Accuracy by Using Histogram of Oriented Gradients (HOG) in Pre-processing Approach. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_6
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DOI: https://doi.org/10.1007/978-981-16-8129-5_6
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