Face Detectors Evaluation to Select the Fastest among DLIB, HAAR Cascade, and MTCNN
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Abstract
Face detection is an important problem in computer vision researchand applications are getting trending due to the advancement in the file of machine learning and computer vision. There are several algorithms and models for face recognition. However, face detection is the first step in all implementations. This research proposed a face detection method based on an enhanced Multi-Task Convolution Neural Network (MTCNN) and improves the network of MTCNN, creates a neural network model based on MTCNN using Python, and cascades to increase the accuracy of face location in difficult scenarios. In this research paper, we evaluated the performance of three famous face detector models on CPU-based machines. Experiments show that HAAR Cascade is the fastest on CPU-based machines but in the case of accuracy, MTCNN is better. MTCNN and DLIB based detectors are designed for GPU-based machines.
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Pakistan Journal Emerging Science and Technologies (PJEST) in collaboration with Govt. Islamia Graduate College Civil Lines Lahore, Pakistan is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
P. Viola and M. Jones, “Robust real-time face detection,” inProceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2005.
A. Benzaoui, H. Bourouba, and A. Boukrouche, “System for automatic faces detection,” in2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), 2012.https://doi.org/10.1109/IPTA.2012.6469545
D. Yang, P. W. C. P. Abeer Alsadoon, A. K. Singh, and A. Elchouemi, “An Emotion Recognition Model Based on Facial Recognition in Virtual Learning Environment”,”Procedia Computer Science, vol. 125, pp. 2–10, 2018.https://doi.org/10.1016/j.procs.2017.12.003
V. Mohanraj,M. Vimalkumar, M. Mithila, and V. Vaidehi, “Robust face recognition system in video using hybrid scale invariant feature transform,”Procedia Comput. Sci., vol. 93, pp. 503–512, 2016.https://doi.org/10.1016/j.procs.2016.07.240
A. Vinayet al., “Face recognition using filtered EOH-sift,”Procedia Computer Science, vol. 79, pp. 543–552, 2016.https://doi.org/10.1016/j.procs.2016.03.069
Page 60of 72
A. H. Abdulnabi, G. Wang, J. Lu, and K. Jia, “Multi-task CNN Model for Attribute Prediction,”arXiv [cs.CV], 2016.https://doi.org /10.1109/TMM.2015.2477680
C. Ding, C. Xu, and D. Tao, “Multi-task pose-invariant face recognition,”IEEE Trans. Image Process., vol. 24, no. 3, pp. 980–993, 2015.https://doi.org /10.1109/TIP.2015.2390959
J. Yim, H. Jung, B. Yoo, C. Choi, D. Park, and J. Kim, “Rotating your face using multi-task deep neural network,” in2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
G. Bradski and A. Kaehler,Learning OpenCV: Computer vision with the OpenCV library. O’Reilly Media, Inc. ", 2008.
S. Liao, A. K. Jain, and S. Z. Li, “A fast and accurate unconstrained face detector,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 211–223, 2016.https://doi.org /10.1109/TPAMI.2015.2448075.
Y. Kortli, M. Jridi, A. A. Falou, and M. Atri, “Facerecognition systems: A survey,”Sensors (Basel), vol. 20, no. 2, p. 342, 2020. https://doi.org/10.3390/s20020342
A. Vinayet al., “Face recognition using filtered EOH-sift,”Procedia Computer Science,vol. 79, pp. 543–552, 2016.https://doi.org/10.1016/j.procs.2016.03.069
M. Xi, L. Chen, D. Polajnar, and W. Tong, “Local binary pattern network: A deep learning approach for face recognition,” in2016 IEEE International Conference on Image Processing (ICIP), 2016.
K. Bonnen, B. F. Klare, and A. K. Jain, “Component-based representation in automated face recognition,”IEEE trans. inf. forensics secur., vol. 8, no. 1, pp. 239–253, 2013.
J. Ren, X. Jiang, and J. Yuan, “Relaxed local ternary pattern for face recognition,” in2013 IEEE International Conference on Image Processing, 2013.
M. Karaaba, O. Surinta, L. Schomaker, and M. A. Wiering, “Robust face recognition by computing distances from multiple histograms of oriented gradients,” in2015 IEEE Symposium Series on Computational Intelligence, 2015.
G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeledfaces in the wild: A database for studying face recognition in unconstrained environments,” 2008.
G. B. Huang, V. Jain, and E. Learned-Miller, “Unsupervised joint alignment of complex images,” in2007 IEEE 11th International Conference on Computer Vision, 2007.
G. Huang, M. Mattar, H. Lee, and E. G. Learned-Miller, “Learning to align from scratch,” 2012, pp. 764–772.
S. Yang, P. Luo, C. C. Loy, and X. Tang, “WIDER FACE: A face detection benchmark,” in2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
H. Jiang and E. Learned-Miller, “Face detection with the faster R-CNN,” in2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), 2017.
Y. Taigman, M. Yang, M. Ranzato, andL. Wolf, “DeepFace: Closing the gap to human-level performance in face verification,” in2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
Y. Bengio,Learning Deep Architectures for AI. Hanover, MD: now, 2009.
C. Zhang and Z. Zhang, “Improving multiview face detection with multi-task deep convolutional neural networks,” inIEEE Winter Conference on Applications of Computer Vision, 2014.https://doi.org /10.1109/WACV.2014.6835990
Z. Zhang, P. Luo, C. C. Loy, and X. Tang, “Facial landmark detection by deep multi-task learning,” inComputer Vision –ECCV 2014, Cham: Springer International Publishing, 2014, pp. 94–108.
Y. Tian, P. Luo, X. Wang, and X. Tang, “Pedestrian detection aided by deep learning semantic tasks,” in2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.https://doi.org/10.1007/978-981-10-3002-4_17
Page 61of 72
A. H. Abdulnabi, G. Wang, J. Lu, and K. Jia, “Multi-Task CNN Model for Attribute Prediction,”IEEE Trans. Multimedia, vol. 17, no. 11, pp. 1949–1959, 2015.https://doi.org/ 10.1109/TMM.2015.2477680
R. Caruana, “Multitask Learning,” inLearning to Learn, Boston, MA: Springer US, 1998, pp. 95–133.https://doi.org/10.1007/978-3-030-37599-7_50
X. Zhu and D. Ramanan, “Face detection, pose estimation, and landmark localization in the wild,” in2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
Www.deeplearningbook. [Online]. Available: http://www.deeplearningbook. [Accessed: 17-Jun-2021].
P. Gong, J. Zhou, W. Fan, and J. Ye, “Efficient multi-task feature learning with calibration,” inProceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining -KDD ’14, 2014.https://doi.org/10.1145/2623330.2623641
G. Levi and T. Hassncer, “Age and gender classification using convolutional neural networks,” in2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015.
R. Ranjan, V. M. Patel, and R. Chellappa, “Hyperface: A deep multi-task learning frameworkfor face detection. landmark localization, pose estimation, and gender recognition.” 2016.
M. Ehrlich, T. J. Shields, T. Almaev, and M. R. Amer, “Facial attributes classification using multi-task representation learning,” in2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016. [35] S. Yang, P. Luo, C.-C. Loy, and X. Tang, “From facial parts responses to face detection: A deep learning approach,” in2015 IEEE International Conference on Computer Vision (ICCV), 2015.
H. Jiang and E. Learned-Miller, “Face detection with the Faster R-CNN,”arXiv [cs.CV], 2016.
A. Kumar, R. Ranjan, V. Patel, and R. Chellappa, “Face alignment by Local Deep Descriptor Regression,”arXiv [cs.CV], 2016.
S. Liao, A. K. Jain, and S. Z. Li, “A fast and accurate unconstrained face detector,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 211–223, 2016.https://doi.org/10.1109/TPAMI.2015.2448075
S. Ren, X. Cao, Y. Wei, and J. Sun, “Face alignment at 3000 FPS via regressing local binary features,” in2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
A. Vinay, D. Hebbar, V. S. Shekhar, K. B. Murthy, and S. Natarajan, “Two novel detector-descriptor-based approaches for face recognition using sift and surf,”Procedia Comput. Sci, vol. 70, pp. 185–197, 2015.https://doi.org/10.1016/j.procs.2015.10.070
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” inProceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2005.https://doi.org/10.1109/CVPR.2001.990517
A. Alfalou, Y. Ouerhani, and C. Brosseau, “Road mark recognition using HOG-SVM and correlation,” inOptics and Photonics for Information Processing XI, 2017.https://doi.org/10.1117/12.2273304
H. J. Seo and P. Milanfar, “Face verification using the LARK representation,”IEEE trans. inf. forensics secure., vol. 6, no. 4, pp. 1275–1286, 2011.https://doi.org/10.1109/TIFS.2011.2159205
T. Napoléon and A. Alfalou, “Pose invariant face recognition: 3D model from single photo,”Opt. Lasers Eng., vol. 89, pp. 150–161, 2017.https://doi.org/10.1016/j.optlaseng.2016.06.019
Q. Wang, D. Xiong, A. Alfalou, and C. Brosseau, “Optical image authentication scheme using dual-polarization decoding configuration,”Opt. Lasers Eng., vol. 112, pp. 151–161, 2019.https://doi.org/10.1016/j.optlaseng.2018.09.008
Y. W. Y. Jia and C. H. M. Turk, “Fisher non-negative matrix factorization for learning local features,” 2004, pp. 27–30.
Page 62of 72
Vinay, D. Hebbar, V. S. Shekhar, K. N. B. Murthy, and S. Natarajan, “Two novel detector-descriptor based approaches for face recognition using SIFT and SURF,”Procedia Comput. Sci., vol. 70, pp. 185–197, 2015.https://doi.org/10.1016/j.procs.2015.10.070
S. U. Hussain, T. Napoléon, and F. Jurie, “Face Recognition using Local Quantized Patterns,” inProceedings of the British Machine Vision Conference 2012, 2012.
F. Smach, J. Miteran, M. Atri, J. Dubois, M. Abid, and J.-P. Gauthier, “An FPGA-based accelerator for Fourier Descriptors computing for color object recognition using SVM,”J. Real-Time Image Process., vol. 2, no. 4, pp. 249–258, 2007.
A. Alfalou and C. Brosseau, “Understanding correlation techniques for face recognition: From basics to applications,” inFace Recognition, InTech, 2010.https://doi.org/10.1016/j.optcom.2013.07.071
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” in2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
I. L. Kambi Beli and C. Guo, “Enhancing face identification using local binary patterns and k-nearest neighbors,”Journal of Imaging, vol. 3, no. 3, p. 37, 2017.https://doi.org/10.3390/jimaging3030037