An Experimental Performance Comparison of Widely Used Face Detection Tools
Abstract Face detection is the task of detecting faces on photos, videos as well as the streaming data such as a webcam. Face detection, which is a specific type of general-purpose object detection, is a key prerequisite for many other artificial intelligence tasks such as face verification, face tagging and retrieval, and face tracking. In addition to that, nowadays, face detection is commonly used in daily routines such as social media, and camera software of smartphones. As a result of this necessity, several face detection tools have been proposed. In this study, an experimental performance comparison of well-known face detection tools in terms of (1) accuracy, and (2) elapsed time of detection, which has become even more critical criteria especially when the face detection mechanism is utilized for a real-time system, is proposed. As a result of this experimental study, it is aimed that shed light on the much-concerned query “which face detection tool provides the best performance?”. In addition to that, this study succeeds in showing that convolutional neural networks achieve great accuracy for face detection.
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He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
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Li, Y., Cai, C., Qiu, G., & Lam, K. M. (2014). Face hallucination based on sparse local-pixel structure. Pattern Recognition, 47(3), 1261–1270. https://doi.org/10.1016/j.patcog.2013.09.012
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep Learning Face Attributes in the Wild. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV ’15), 3730–3738. https://doi.org/10.1109/ICCV.2015.425
Ma, J., Zhao, J., Ma, Y., & Tian, J. (2015). Non-rigid visible and infrared face registration via regularized Gaussian fields criterion. Pattern Recognition, 48(3), 772–784. https://doi.org/10.1016/j.patcog.2014.09.005
Mehta, J., Ramnani, E., & Singh, S. (2018). Face Detection and Tagging Using Deep Learning. 2nd International Conference on Computer, Communication, and Signal Processing: Special Focus on Technology and Innovation for Smart Environment (ICCCSP 2018), 1–6. https://doi.org/10.1109/ICCCSP.2018.8452853
Moghaddam, B., & Pentland, A. (1997). Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 696–710. https://doi.org/10.1109/34.598227
Momtahina, Hossain, R., Rahman, M. M., & Tania, O. A. (2019). Image Capturing and Automatic Face Recognition. United International University.
Nguyen, T. (2019). sthanhng/yoloface: Deep learning-based Face detection using the YOLOv3 algorithm. Retrieved July 23, 2019, from https://github.com/sthanhng/yoloface
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Osuna, E., Freund, R., & Girosit, F. (2002). Training support vector machines: an application to face detection. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.1997.609310
Ou, W., You, X., Tao, D., Zhang, P., Tang, Y., & Zhu, Z. (2014). Robust face recognition via occlusion dictionary learning. Pattern Recognition, 47(4), 1559–1572. https://doi.org/10.1016/j.patcog.2013.10.017
Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. ArXiv, 1–6.
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Yi Ma. (2009). Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 210–227. https://doi.org/10.1109/TPAMI.2008.79
Yang, M. H., Kriegman, D. J., & Ahuja, N. (2002). Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 34–58. https://doi.org/10.1109/34.982883
Yang, S., Luo, P., Loy, C. C., & Tang, X. (2016). WIDER FACE: A face detection benchmark. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. https://doi.org/10.1109/CVPR.2016.596
Zafeiriou, S., Zhang, C., & Zhang, Z. (2015). A survey on face detection in the wild: Past, present and future. Computer Vision and Image Understanding, 138, 1–24. https://doi.org/10.1016/j.cviu.2015.03.015
Zhang, C., & Zhang, Z. (2014). Improving multiview face detection with multi-task deep convolutional neural networks. 2014 IEEE Winter Conference on Applications of Computer Vision (WACV 2014), 1036–1041. https://doi.org/10.1109/WACV.2014.6835990
Zhang, Y., Ding, M., Bai, Y., & Ghanem, B. (2019). Detecting small faces in the wild based on generative adversarial network and contextual information. Pattern Recognition, 97, 74–86. https://doi.org/10.1016/j.patcog.2019.05.023
Zheng, Y., Zhu, C., Luu, K., Bhagavatula, C., Le, T. H. N., & Savvides, M. (2016). Towards a Deep Learning Framework for Unconstrained Face Detection. ArXiv Preprint, 1612.05322, 1–8.
Zhong Wu, Qifa Ke, Jian Sun, & Heung-Yeung Shum. (2010). Scalable Face Image Retrieval with Identity-Based Quantization and Multireference Reranking. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3469–3476. https://doi.org/10.1109/tpami.2011.111
Crowley, J. L., & Berard, F. (1997). Multi-modal tracking of faces for video communications. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 640–645. https://doi.org/10.1109/cvpr.1997.609393
Culjak, I., Abram, D., Pribanic, T., Dzapo, H., & Cifrek, M. (2012). A brief introduction to OpenCV. Proceedings of the 35th International Convention MIPRO (MIPRO 2012), 1725–1730. Opatija, Croatia: IEEE.
Darrell, T., Gordon, G., Harville, M., & Woodfill, J. (2000). Integrated person tracking using stereo, color, and pattern detection. International Journal of Computer Vision, 37(2), 175–185. https://doi.org/10.1023/A:1008103604354
Donato, G., Bartlett, M. S., Hager, J. C., Ekman, P., & Sejnowski, T. J. (1999). Classifying facial actions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(10), 974–989. https://doi.org/10.1109/34.799905
Edwards, G. J., Taylor, C. J., & Cootes, T. F. (1998). Learning to identify and track faces in image sequences. Proceedings of Sixth International Conference on Computer Vision, 317–322. https://doi.org/10.1109/AFGR.1998.670958
Essa, I. A., & Pentland, A. P. (2002). Facial expression recognition using a dynamic model and motion energy. Proceedings of 5th IEEE International Conference on Computer Vision, 360–367. https://doi.org/10.1109/iccv.1995.466916
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010). The PASCAL Visual Object Classes (VOC) Challenge. International Journal of Computer Vision, 88(2), 303–338. https://doi.org/10.1007/s11263-009-0275-4
Gao, Y., & Qi, Y. (2005). Robust visual similarity retrieval in single model face databases. Pattern Recognition, 38(7), 1009–1020. https://doi.org/10.1016/j.patcog.2004.12.006
Geitgey, A. (2019). ageitgey/face_recognition: The world’s simplest facial recognition api for Python and the command line. Retrieved July 23, 2019, from https://github.com/ageitgey/face_recognition
Gunay, M., & Ensari, T. (2017). Comparison of face recognition algorithms. 2017 25th Signal Processing and Communications Applications Conference (SIU), 1–4. https://doi.org/10.1109/SIU.2017.7960469
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
Heinisuo, O.-P. (2019). opencv-python. Retrieved July 23, 2019, from https://pypi.org/project/opencv-python/
Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. (2007). Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments.
Jain, V., & Learned-Miller, E. (2010). FDDB: A Benchmark for Face Detection in Unconstrained Settings.
Jung, S.-G., An, J., Kwak, H., Salminen, J., & Jansen, B. J. (2018). Assessing the Accuracy of Four Popular Face Recognition Tools for Inferring Gender, Age, and Race. Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM-18), 624–627. Stanford, California, USA: AAAI Press.
Kayikci, S. (2018). A Deep Learning Method for Passing Completely Automated Public Turing Test. 3rd International Conference on Computer Science and Engineering (UBMK 2018), 41–44. https://doi.org/10.1109/UBMK.2018.8566318
King, D. (2017). dlib C++ Library: High Quality Face Recognition with Deep Metric Learning. Retrieved July 23, 2019, from http://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html
King, D. (2019). dlib C++ Library. Retrieved July 23, 2019, from http://dlib.net
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (NIPS’12), 1097–1105. Lake Tahoe, Nevada.
Lam, K.-M., & Yan, H. (1994). Fast algorithm for locating head boundaries. Journal of Electronic Imaging, 3(4), 351–359. https://doi.org/10.1117/12.183806
Li, H., Lin, Z., Shen, X., Brandt, J., & Hua, G. (2015). A Convolutional Neural Network Cascade for Face Detection. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’15), 5325–5334. https://doi.org/10.1109/CVPR.2015.7299170
Li, Y., Cai, C., Qiu, G., & Lam, K. M. (2014). Face hallucination based on sparse local-pixel structure. Pattern Recognition, 47(3), 1261–1270. https://doi.org/10.1016/j.patcog.2013.09.012
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep Learning Face Attributes in the Wild. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV ’15), 3730–3738. https://doi.org/10.1109/ICCV.2015.425
Ma, J., Zhao, J., Ma, Y., & Tian, J. (2015). Non-rigid visible and infrared face registration via regularized Gaussian fields criterion. Pattern Recognition, 48(3), 772–784. https://doi.org/10.1016/j.patcog.2014.09.005
Mehta, J., Ramnani, E., & Singh, S. (2018). Face Detection and Tagging Using Deep Learning. 2nd International Conference on Computer, Communication, and Signal Processing: Special Focus on Technology and Innovation for Smart Environment (ICCCSP 2018), 1–6. https://doi.org/10.1109/ICCCSP.2018.8452853
Moghaddam, B., & Pentland, A. (1997). Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 696–710. https://doi.org/10.1109/34.598227
Momtahina, Hossain, R., Rahman, M. M., & Tania, O. A. (2019). Image Capturing and Automatic Face Recognition. United International University.
Nguyen, T. (2019). sthanhng/yoloface: Deep learning-based Face detection using the YOLOv3 algorithm. Retrieved July 23, 2019, from https://github.com/sthanhng/yoloface
OpenCV. (2019). Retrieved July 22, 2019, from https://opencv.org
Osuna, E., Freund, R., & Girosit, F. (2002). Training support vector machines: an application to face detection. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.1997.609310
Ou, W., You, X., Tao, D., Zhang, P., Tang, Y., & Zhu, Z. (2014). Robust face recognition via occlusion dictionary learning. Pattern Recognition, 47(4), 1559–1572. https://doi.org/10.1016/j.patcog.2013.10.017
Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. ArXiv, 1–6.
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Yi Ma. (2009). Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 210–227. https://doi.org/10.1109/TPAMI.2008.79
Yang, M. H., Kriegman, D. J., & Ahuja, N. (2002). Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 34–58. https://doi.org/10.1109/34.982883
Yang, S., Luo, P., Loy, C. C., & Tang, X. (2016). WIDER FACE: A face detection benchmark. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. https://doi.org/10.1109/CVPR.2016.596
Zafeiriou, S., Zhang, C., & Zhang, Z. (2015). A survey on face detection in the wild: Past, present and future. Computer Vision and Image Understanding, 138, 1–24. https://doi.org/10.1016/j.cviu.2015.03.015
Zhang, C., & Zhang, Z. (2014). Improving multiview face detection with multi-task deep convolutional neural networks. 2014 IEEE Winter Conference on Applications of Computer Vision (WACV 2014), 1036–1041. https://doi.org/10.1109/WACV.2014.6835990
Zhang, Y., Ding, M., Bai, Y., & Ghanem, B. (2019). Detecting small faces in the wild based on generative adversarial network and contextual information. Pattern Recognition, 97, 74–86. https://doi.org/10.1016/j.patcog.2019.05.023
Zheng, Y., Zhu, C., Luu, K., Bhagavatula, C., Le, T. H. N., & Savvides, M. (2016). Towards a Deep Learning Framework for Unconstrained Face Detection. ArXiv Preprint, 1612.05322, 1–8.
Zhong Wu, Qifa Ke, Jian Sun, & Heung-Yeung Shum. (2010). Scalable Face Image Retrieval with Identity-Based Quantization and Multireference Reranking. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3469–3476. https://doi.org/10.1109/tpami.2011.111
Kabakus, A. T. (2019). An Experimental Performance Comparison of Widely Used Face Detection Tools. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(3), 5–12. https://doi.org/10.14201/ADCAIJ201983512
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