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Paper Title : Face Detection Using YOLO

ISSN : 2395-1303
Year of Publication : 2022
10.5281/zenodo.6567441

Authors: Abhishek Rana

         


Abstract
Deep learning is a buzzword these days, and it’s a new phase of machine learning that teaches computers to detect patterns in enormous amounts of data. It primarily describes learning at several levels of representation, which aids in making understanding of text, voice, and visual data. Many businesses use a convolutional neural network, a sort of deep learning, to deal with the objects in a video sequence. Deep Convolution Neural Networks (CNNs) have demonstrated excellent performance in terms of object detection, picture classification, and semantic segmentation. Object detection is described as the process of classifying and locating objects. Face detection is one of the most difficult pattern recognition issues. Deep learning is a new phase of machine learning that teaches computers to find patterns in massive volumes of data, and it’s a buzzword these days. It mostly refers to learning at several levels of representation, which aids in the comprehension of text, audio, and visual data. To deal with the objects in a video sequence, many firms utilise a convolutional neural network, which is a type of deep learning. In terms of object detection, picture categorization, and semantic segmentation, Deep Convolution Neural Networks (CNNs) have shown to be quite effective. The process of classifying and locating things is referred to as object detection. One of the most difficult pattern recognition problems is face detection.

Reference
[1] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. [2] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Proceedings of the 25th International Conference on Neural Information Processing Sy [3] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580–587. [4] R. Girshick, “Fast R-CNN,” Proc. IEEE International Conference on Computer Vision, ICCV 2015, pp. 1440–1448, 2015. [5] S. Ren, K. He, R. Girshick, and J. Sun, “Faster RCNN: towards realtime object detection with region proposal networks,” Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1. MIT Press, pp. 91–99, 2015. [6] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” 2015. [7] R. Vaillant, C. Monrocq, and Y. Lecun, “Original approach for the localisation of objects in images,” IEEE Proceedings on Vision, Image, and Signal Processing, vol. 4, 1994. [8] H.A. Rowley, S. Baluja, T. Kanade, “Neural networkbased face detection”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 1, pp. 23–38, 1998. [9] C. Garcia and M. Delakis, ”A neural architecture for fast and robust face detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 11, pp. 1408–1423, 2004. [10] M. Osadchy, Y. Le Cun, and M. L. Miller, “Synergistic Face Detection and Pose Estimation with Energy-Based Models,” Journal of Machine Learning Research, vol. 8, pp. 1197-1215, 2007. [11] F. J. Phillip Ian, “Facial feature detection using Haar classifiers,” J. Comput. Sci. Coll., vol. 21, no. 4, pp. 127–133, 2002. [12] H. Li, Z. Lin, X. Shen, J. Brandt, and G. Hua, “A Convolutional Neural Network Cascade for Face Detection.”, IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2015, pp. 5325-5334, 2015. [13] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes (VOC) Challenge”, International Journal of Computer Vision, vol. 88, no. 2, pp. 303-338, 2010. [14] T.-Y. Lin et al., “Microsoft COCO: Common Objects in Context,” European Conference on Computer Vision, ECCV 2014, Lecture Notes in Computer Science, vol 8693. Springer, Cham, pp. 740-755. [15] C. Szegedy, A. Toshev, and D. Erhan, “Deep neural networks for object detection,” Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. Curran Associates Inc., pp. 2553–2561, 2013. [16] V. Jain and E. Learned-Miller, “FDDB: A Benchmark for Face Detection in Unconstrained Settings.”, Technical Report UM-CS-2010-009, Dept. of Computer Science, University of Massachusetts, Amherst. 2010.
Keywords
- YOLO,FACE ,Detection.