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  •   Hashiru Isiaka Muhammad

  •   Kabir Ibrahim Musa

  •   Mustapha Lawal Abdulrahman

  •   Abdullahi Abubakar

  •   Kabiru Umar

  •   Abdulhakeem Ishola

Abstract

In this paper, we present a new face detection scheme using deep learning and achieving state-of-the-art recognition performance using real-world datasets.  We designed and implemented a face recognition system using Principal Component Analysis (PCA) and Faster R Convolutional Neural Network (Faster R CNN). In particular, we improve the state-of-the-art Faster RCNN framework by using Principal Component Analysis (PCA) technique and Faster R CNN to detect and recognise faces in a face database.  The Principal Component Analysis (PCA) was used to extract features and dimensionality reduction from the face database, while the Faster R Convolutional Neural Network algorithm was used to identify patterns in the dataset via training the neural network. The three real-world datasets used in our experiment are ORL, Yale, and California face dataset. When implemented on the ORL face dataset, the algorithm achieved average recognition accuracy of 99%, with a recognition time of 147.72 seconds for 10 runs, and the recognition time/image was 0.3 sec/image on 400 images. The Yale face dataset achieved average recognition accuracy of 99.24% with a recognition time of 63.45 seconds for 10 runs, and the recognition time/image was 0.53 sec/image on 120 images. Finally, on California Face Database (CFD), it achieved average recognition accuracy of 99.52% with a recognition time of 226.05 seconds for 10 runs, and the recognition time/image was 0.27 sec/image on 827 images. On the CFD dataset, however, the proposed approach has excellent classification performance when the recall ratio is high. The proposed method achieves a higher recall and accuracy ratio than the Faster RCNN without PCA method. For the F-score, the proposed method achieved 0.98, which is significantly higher than the 0.95 achieved by the Faster-RCNN. This demonstrates the superiority of our model performance-wise as against state-of-the-art, both in terms of accuracy and fast recognition. Therefore our model is more efficient when compared to the latest researches done in the area of facial recognition.

Keywords: Face Recognition, Convolutional Neural Network (CNN), Faster R-CNN, Principal Component Analysis (PCA), PCA-Faster R-CNN

References

Ahmed, A., Guo, J., Ali, F., Deeba, F., & Ahmed, A. (2018). LBPH based improved face recognition at low resolution. Paper presented at the 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD).

Allagwail, S., Gedik, O. S., & Rahebi, J. (2019). Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter. Symmetry, 11(2), 157.

Sakagami, Y., Watanabe, R., Aoyama, C., Matsunaga, S., Higaki, N., & Fujimura, K. (2002). The intelligent ASIMO: System overview and integration. In IEEE/RSJ international conference on intelligent robots and systems (Vol. 3, pp. 2478-2483). IEEE.

Breuer, R., & Kimmel, R. (2017). A deep learning perspective on the origin of facial expressions. arXiv preprint arXiv:1705.01842.

Chen, D., Hua, G., Wen, F., & Sun, J. (2016). Supervised transformer network for efficient face detection. Paper presented at the European Conference on Computer Vision.

Chen, J., Luo, Z., Takiguchi, T., & Ariki, Y. (2016). Multithreading cascade of SURF for facial expression recognition. EURASIP Journal on Image and Video Processing, 2016(1), 37.

Co?kun, M., Uçar, A., Yildirim, Ö., & Demir, Y. (2017). Face recognition based on convolutional neural network. Paper presented at the 2017 International Conference on Modern Electrical and Energy Systems (MEES).

DEFFO, L. L. S., FUTE, E. T., & Tonye, E. CNNSFR: A Convolutional Neural Network System for Face Detection and Recognition.

Girshick, R. (2015). Fast r-CNN. Paper presented at the Proceedings of the IEEE international conference on computer vision.

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.

Guo, G., Wang, H., Yan, Y., Zheng, J., & Li, B. (2019). A fast face detection method via a convolutional neural network. Neurocomputing.

Guo, G., Wang, H., Zhao, W.-L., Yan, Y., & Li, X. (2017). Object discovery via cohesion measurement. IEEE transactions on cybernetics, 48(3), 862-875.

Hao, L., & Jiang, F. (2018). A New Facial Detection Model based on the Faster R-CNN. Paper presented at the IOP Conference Series: Materials Science and Engineering.

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 37(9), 1904-1916.

Huang, J., Shang, Y., & Chen, H. (2019). Improved Viola-Jones face detection algorithm based on HoloLens. EURASIP Journal on Image and Video Processing, 2019(1), 41.

Humne, S., & Sorte, P. (2018). A Review on Face Recognition using Local Binary Pattern Algorithm.

Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., & Keutzer, K. (2014). Densenet: Implementing efficient convnet descriptor pyramids. arXiv preprint arXiv:1404.1869.

Jiang, H., & Learned-Miller, E. (2017). Face detection with the faster R-CNN. Paper presented at the 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

Kamil, I. A., & Are, A. S. (2018). Makeup-Invariant Face Recognition using combined Gabor Filter Bank and Histogram of Oriented Gradients. Paper presented at the Proceedings of the 2nd International Conference on Advances in Image Processing.

Ko, B. (2018). A brief review of facial emotion recognition based on visual information. sensors, 18(2), 401.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Paper presented at the Advances in neural information processing systems.

Li, C., Diao, Y., Ma, H., & Li, Y. (2008). A statistical PCA method for face recognition. Paper presented at the 2008 Second international symposium on intelligent information technology application.

Li, C., Wang, R., Li, J., & Fei, L. (2020). Face detection based on YOLOv3. In Recent Trends in Intelligent Computing, Communication and Devices (pp. 277-284). Springer, Singapore.

Li, J., Zhao, B., & Zhang, H. (2009). Face recognition based on PCA and LDA combination feature extraction. Paper presented at the 2009 First International Conference on Information Science and Engineering.

Liqiao, J., & Runhe, Q. (2017). Face recognition based on adaptive weighted HOG. Computer Enigeering and Applications, 53(3), 164-168.

Nam, H., & Han, B. (2016). Learning multi-domain convolutional neural networks for visual tracking. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.

Rahim, R., Afriliansyah, T., Winata, H., Nofriansyah, D., & Aryza, S. (2018). Research of Face Recognition with Fisher Linear Discriminant. Paper presented at the IOP Conference Series: Materials Science and Engineering.

Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Paper presented at the Advances in neural information processing systems.

Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013). Overfeat: Integrated recognition, localisation and detection using convolutional networks. arXiv preprint arXiv:1312.6229.

Sun, Y., & Yu, J. (2017). Facial expression recognition by fusing Gabor and Local Binary Pattern features. Paper presented at the International Conference on Multimedia Modeling.

Taloba, A. I., Sewisy, A. A., & Dawood, Y. A. (2018). Accuracy Enhancement Scaling Factor of Viola-Jones Using Genetic Algorithms. Paper presented at the 2018 14th International Computer Engineering Conference (ICENCO).

Triantafyllidou, D., Nousi, P., & Tefas, A. (2018). Fast deep convolutional face detection in the wild exploiting hard sample mining. Big data research, 11, 65-76.

Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International journal of computer vision, 104(2), 154-171.

Valueva, M. V., Nagornov, N., Lyakhov, P. A., Valuev, G. V., & Chervyakov, N. I. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation, 177, 232-243.

Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International journal of computer vision, 57(2), 137-154.

Wan, S., Chen, Z., Zhang, T., Zhang, B., & Wong, K.-k. (2016). Bootstrapping face detection with hard negative examples. arXiv preprint arXiv:1608.02236.

Yang, P., & Yang, G. (2016). Feature extraction using dual-tree complex wavelet transform and gray level co-occurrence matrix. Neurocomputing, 197, 212-220.

Zhang, S., Zhu, X., Lei, Z., Wang, X., Shi, H., & Li, S. Z. (2018). Detecting face with densely connected face proposal network. Neurocomputing, 284, 119-127.

Zitnick, C. L., & Dollár, P. (2014). Edge boxes: Locating object proposals from edges. Paper presented at the European conference on computer vision.

Bernstein B.(2017). Principal Component Analysis, Lecture slides, available online ://davidrosenberg.github.io/mlcourse/Archive/2017/Lectures/13-PCA-Slides.pdf [accessed on 19 January, 2021].

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How to Cite
[1]
Muhammad, H.I., Musa, K.I., Abdulrahman, M.L., Abubakar, A., Umar, K. and Ishola, A. 2021. Enhancing Detection Performance of Face Recognition Algorithm Using PCA-Faster R-CNN. European Journal of Electrical Engineering and Computer Science. 5, 3 (May 2021), 9-16. DOI:https://doi.org/10.24018/ejece.2021.5.3.321.

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