Max Feature Map CNN with Support Vector Guided Softmax for Face Recognition

Herdianti Darwis - Universitas Muslim Indonesia, Makassar, 90235, Indonesia
Zahrizhal Ali - Universitas Muslim Indonesia, Makassar, 90235, Indonesia
Yulita Salim - Universitas Muslim Indonesia, Makassar, 90235, Indonesia
Poetri Lestari Belluano - Universitas Muslim Indonesia, Makassar, 90235, Indonesia

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Face recognition has made significant progress because of advances in deep convolutional neural networks (CNNs) in addressing face verification in large amounts of data variation. When image data comes from different sources and devices, the identifiability of other classes and the presence of profile face data can lead to inaccurate and ambiguous classification because other classes lack discriminatory power. Furthermore, using a complex architecture with many deep convolutional layers can become very slow in the training process due to a huge amount of Random Access Memory (RAM) usage during the reverse pass of backpropagation. In this paper, we design a light CNN architecture that addresses these challenges. Specifically, we implemented Max-feature-map (MFM) into each convolutional layer to improve the accuracy and efficiency of the CNN. The strength of the support vector-guided SoftMax (SV-SoftMax) is also used in the proposed method to emphasize misclassified points and adaptively guide feature learning. Experimental results show that the 9-Layers CNN with MFM layer and SV-SoftMax outperform VGG-19 with 96.22% validation accuracy and the second rank below FaceNet tested on the same dataset with fewer parameters. Moreover, the model performed well on data that is obtained from various capture devices such as webcam, CCTVs, phone cameras, and DSLR cameras. The implications of this research could extend to scenarios requiring face recognition technology implementation with light size, such as surveillance and authentication systems


Convolutional neural network; face recognition; SoftMax; deep learning.

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N. Mohammad, A. M. Muad, R. Ahmad, and M. Y. P. M. Yusof, "Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging," BMC Med. Imaging, vol. 22, no. 1, 2022, doi: 10.1186/s12880-022-00794-6.

I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard, "The MegaFace benchmark: 1 million faces for recognition at scale," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016. doi: 10.1109/CVPR.2016.527.

X. Wang, S. Wang, S. Zhang, T. Fu, H. Shi, and T. Mei, "Mis-Classified Vector Guided Softmax Loss for Face Recognition Xiaobo," arXiv, 2018.

X. Wu, R. He, Z. Sun, and T. Tan, "A light CNN for deep face representation with noisy labels," IEEE Trans. Inf. Forensics Secur., vol. 13, no. 11, 2018, doi: 10.1109/TIFS.2018.2833032.

I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio, "Maxout networks," in 30th International Conference on Machine Learning, ICML 2013, 2013.

J. Deng, J. Guo, J. Yang, N. Xue, I. Kotsia, and S. Zafeiriou, "ArcFace: Additive Angular Margin Loss for Deep Face Recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 10, 2022, doi: 10.1109/TPAMI.2021.3087709.

F. Wang, J. Cheng, W. Liu, and H. Liu, "Additive Margin Softmax for Face Verification," IEEE Signal Process. Lett., vol. 25, no. 7, 2018, doi: 10.1109/LSP.2018.2822810.

X. Wang, S. Zhang, Z. Lei, S. Liu, X. Guo, and S. Z. Li, "Ensemble soft-margin softmax loss for image classification," in IJCAI International Joint Conference on Artificial Intelligence, 2018. doi: 10.24963/ijcai.2018/138.

F. Huang, M. Yang, X. Lv, and F. Wu, "Cosmos-loss: A face representation approach with independent supervision," IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3062069.

Y. Sun, Y. Chen, X. Wang, and X. Tang, "Deep learning face representation by joint identification-verification," in Advances in Neural Information Processing Systems, 2014.

G. Gao, Y. Yu, J. Yang, G. J. Qi, and M. Yang, "Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition," IEEE Trans. Circuits Syst. Video Technol., vol. 32, no. 5, 2022, doi: 10.1109/TCSVT.2020.3042178.

Y. Dong, C. Yang, and Y. Zhang, "Deep metric learning with online hard mining for hyperspectral classification," Remote Sens., vol. 13, no. 7, 2021, doi: 10.3390/rs13071368.

T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal Loss for Dense Object Detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 2, 2020, doi: 10.1109/TPAMI.2018.2858826.

X. Li, C. Lv, W. Wang, G. Li, L. Yang, and J. Yang, "Generalized Focal Loss: Towards Efficient Representation Learning for Dense Object Detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 3, 2023, doi: 10.1109/TPAMI.2022.3180392.

W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, "SphereFace: Deep hypersphere embedding for face recognition," in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017. doi: 10.1109/CVPR.2017.713.

W. Liu, Y. Wen, B. Raj, R. Singh, and A. Weller, "SphereFace Revived: Unifying Hyperspherical Face Recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 2, 2023, doi: 10.1109/TPAMI.2022.3159732.

Z. Zhang, W. Lu, X. Feng, J. Cao, and G. Xie, "A Discriminative Feature Learning Approach With Distinguishable Distance Metrics for Remote Sensing Image Classification and Retrieval," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 16, 2023, doi: 10.1109/JSTARS.2022.3233032.

A. POPIELARSKA, "ã€LFW】Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments," Neurol. Neurochir. Psychiatr. Pol., vol. 5, no. 3, 1955.

Y. Sun, X. Wang, and X. Tang, "Deeply learned face representations are sparse, selective, and robust," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015. doi: 10.1109/CVPR.2015.7298907.

F. Schroff, D. Kalenichenko, and J. Philbin, "FaceNet: A unified embedding for face recognition and clustering," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015. doi: 10.1109/CVPR.2015.7298682.

K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2015.

H. Pranoto and O. Kusumawardani, "Real-time triplet loss embedding face recognition for authentication student attendance records system framework," Int. J. Informatics Vis., vol. 5, no. 2, 2021, doi: 10.30630/joiv.5.2.480.

X. Liu, H. Wang, and Z. Li, "An Approach for Deep Learning in ECG Classification Tasks in the Presence of Noisy Labels," in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2021. doi: 10.1109/EMBC46164.2021.9630763.

S. Zagoruyko and N. Komodakis, "Wide Residual Networks," in British Machine Vision Conference 2016, BMVC 2016, 2016. doi: 10.5244/C.30.87.

K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016. doi: 10.1109/CVPR.2016.90.

H. Feng, V. Misra, and D. Rubenstein, "The CIFAR-10 dataset," Electr. Eng., vol. 35, no. 1, 2007.

Y. Zheng, B. Wang, and Y. Zheng, "68 Face Feature Points Detection Based on Cascading Convolutional Neural Network with Small Filter," Highlights Sci. Eng. Technol., vol. 9, 2022, doi: 10.54097/hset.v9i.1731.

V. Thambawita, I. Strümke, S. A. Hicks, P. Halvorsen, S. Parasa, and M. A. Riegler, "Impact of image resolution on deep learning performance in endoscopy image classification: An experimental study using a large dataset of endoscopic images," Diagnostics, vol. 11, no. 12, 2021, doi: 10.3390/diagnostics11122183.

L. Parisi, D. Neagu, R. Ma, and F. Campean, "Quantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson's disease and COVID-19," Expert Syst. Appl., vol. 187, 2022, doi: 10.1016/j.eswa.2021.115892.

S. Sheena and S. Mathew, "Performance Evaluation of New Feature based on Ordinal Pattern Analysis for Iris Biometric Recognition," Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 10, 2022, doi: 10.14569/IJACSA.2022.0131058.

H. Alaeddine and M. Jihene, "Deep network in network," Neural Comput. Appl., vol. 33, no. 5, 2021, doi: 10.1007/s00521-020-05008-0.

I. Kandel and M. Castelli, "The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset," ICT Express, vol. 6, no. 4, 2020, doi: 10.1016/j.icte.2020.04.010.

B. Pang, E. Nijkamp, and Y. N. Wu, "Deep Learning With TensorFlow: A Review," Journal of Educational and Behavioral Statistics, vol. 45, no. 2. 2020. doi: 10.3102/1076998619872761.