ICTACT Journal on Image and Video Processing

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Canny Edge Detection and Contrast Stretching for Facial Expression Detection and Recognition Using Machine Learning


Affiliations
1 PG and Research Department of Computer Science Engineering, Dr. N.G.P. Arts and Science College, India
     

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Facial expression recognition in the world is challenging due to various unconstrained conditions. Although existing facial expression classifiers have been almost perfect for analyzing constrained frontal faces, they fail to perform well on partially occluded faces common in the wild. This paper deals with Facial expression detection and recognition through the Viola-jones algorithm and HCNN using the LSTM method to avoid those challenges in recent work. For face detection, basically, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and it helps to perform the feature selection through the whale optimization algorithm, once after compression and further, it minimizes the feature vector given into the HCNN and LSTM model for efficiently identifying the facial expression. In existing work, the feature extraction stage exact finding of the corner becomes a very difficult task, to solve this issue need to use a corner detection algorithm to enhance the feature extraction (corner points) from the face image. One of the main drawbacks of WOA is that it is not good at exploring the search space. To overcome those issues, the work introduced an improved framework for facial image recognition. In which first edges are detected using the canny edge detection operator. Then improved linear contrast stretching is used for image enhancement. Then in feature extraction, the author proposes Hybrid SIFT with Double δ-LBP (Dδ-LBP) to obtain the features that are illumination and pose independent. For face detection, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and significant features are selected by using self-learning chicken swarm optimization, it minimizes the feature vector is given into the Hybrid HCNN and LSTM model for efficiently identifying the facial expression. Experimental results demonstrate the efficiency of the proposed work in terms of accuracy, precision, recall and f-measure.

Keywords

Viola-Jones Algorithm, Face Detection, Edge Detection, Feature Matching, Artificial Bee Colony, Hybrid Convolutional Neural Network
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  • N. Zeng, H. Zhang and A.M. Dobaie, “Facial Expression Recognition Via Learning Deep Sparse Auto Encoders”, Neuro Computing, Vol. 273, pp. 643-649, 2018.

  • N.B. Kar, K.S. Babu and S.K. Jena, “Face Expression Recognition using Histograms of Oriented Gradients with Reduced Features”, Proceedings of International Conference on Computer Vision and Image Processing, pp. 209-219, 2017.

  • S. Li and W. Deng, “Reliable Crowd Sourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition”, IEEE Transactions on Image Processing, Vol. 28, No. 1, pp. 356-370, 2018.

  • K. Zhang, Y. Huang, Y. Du and L. Wang, “Facial Expression Recognition based on Deep Evolutional SpatialTemporal Networks”, IEEE Transactions on Image Processing, Vol. 26, No. 9, pp. 4193-4203, 2017.

  • K. Shan, J. Guo and R. Bie, “Automatic Facial Expression Recognition based on A Deep Convolutional-NeuralNetwork Structure”, Proceedings of International Conference on Software Engineering Research, Management and Applications, pp. 123-128, 2017.

  • K. Wang, X. Peng, J. Yang and Y. Qiao, “Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition”, IEEE Transactions on Image Processing, Vol. 29, pp. 4057-4069, 2020.

  • S. Nigam, R. Singh and A.K. Misra, “Efficient Facial Expression Recognition using Histogram of Oriented Gradients in Wavelet Domain”, Multimedia Tools and Applications, Vol. 77, No. 21, pp. 28725-28747, 2018.

  • Y.H. Lai and S.H. Lai, “Emotion-Preserving Representation Learning Via Generative Adversarial Network for MultiView Facial Expression Recognition”, Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 263-270, 2018.

  • J.C. Batista, V. Albiero and L. Silva, “Aumpnet: Simultaneous Action Units Detection and Intensity Estimation on Multipose Facial Images using a Single Convolutional Neural Network”, Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 866-871, 2017.

  • E. Sariyanidi, H. Gunes and A. Cavallaro, “Learning bases of Activity for Facial Expression Recognition”, IEEE Transactions on Image Processing, Vol. 26, No. 4, pp. 19651978, 2017.

  • S. Kumar, S. Singh and J. Kumar, “Automatic Live Facial Expression Detection using Genetic Algorithm with HAAR Wavelet Features and SVM”, Wireless Personal Communications, Vol. 103, No. 3, pp. 2435-2453, 2018.

  • I. Gogic and M. Manhart, “Fast Facial Expression Recognition using Local Binary Features and Shallow Neural Networks”, The Visual Computer, Vol. 36, No. 1, pp. 97-112, 2020.

  • H. Yang, Z. Zhang and L. Yin, “Identity-Adaptive Facial Expression Recognition through Expression Regeneration using Conditional Generative Adversarial Networks”, Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 294-301, 2018.

  • Y. Ding, Q. Zhao, B. Li and X. Yuan, “Facial Expression Recognition from Image Sequence based on LBP and Taylor Expansion”, IEEE Access, Vol. 5, pp. 19409-19419, 2017.

  • S.K.A. Kamarol and R. Parthiban, “Joint Facial Expression Recognition and Intensity Estimation based on Weighted Votes of Image Sequences”, Pattern Recognition Letters, Vol. 92, pp. 25-32, 2017.

  • J.H. Shah, M. Sharif, M. Yasmin and S.L. Fernandes, “Facial Expressions Classification and False Label Reduction using LDA and Threefold SVM”, Pattern Recognition Letters, Vol. 139, pp. 166-173, 2020.

  • Qi, C., Li, M., Wang, Q., Zhang, H., Xing, J., Gao, Z. and Zhang, H., 2018. Facial expressions recognition based on cognition and mapped binary patterns. IEEE Access, 6, pp.18795-18803.

  • G. Shi, J. Suo and C. Liu, “Moving Target Detection Algorithm in Image Sequences based on Edge Detection and Frame Difference”, Proceedings of International Conference on Information Technology and Mechatronics Engineering, pp. 740-744, 2017.

  • K. Gaurav and U. Ghanekar, “Image Steganography based on Canny Edge Detection, Dilation Operator and Hybrid Coding”, Journal of Information Security and Applications, Vol. 41, pp. 41-51, 2018.

  • G. Deng and Y. Wu, “Double Lane Line Edge Detection Method based on Constraint Conditions Hough Transform”, Proceedings of International on Symposium on Distributed Computing and Applications for Business Engineering and Science, pp. 107-110, 2018.

  • S. Bhairannawar, A. Patil, A. Janmane and M. Huilgol, “Color Image Enhancement using Laplacian Filter and Contrast Limited Adaptive Histogram Equalization”, Proceedings of International on Innovations in Power and Advanced Computing Technologies, pp. 1-5, 2017.

  • Z. Shi, Y. Feng and L. He, “Let You See in Sand Dust Weather: A Method based on Halo-Reduced Dark Channel Prior Dehazing for Sand-Dust Image Enhancement”, IEEE Access, Vol. 7, pp. 116722-116733, 2019.

  • A. Mustapha, A. Oulefki, M. Bengherabi and M.A. Algaet, “Towards Nonuniform Illumination Face Enhancement Via Adaptive Contrast Stretching”, Multimedia Tools and Applications, Vol. 76, No. 21, pp. 21961-21999, 2017.

  • S. Deb, X.Z. Gao, K. Tammi and P. Mahanta, “Recent Studies on Chicken Swarm Optimization Algorithm: A Review”, Artificial Intelligence Review, Vol. 53, pp. 1-29, 2019.

  • J. Wang, Z. Cheng and Y. Bi, “Improvement and Application of Chicken Swarm Optimization for Constrained Optimization”, IEEE Access, Vol. 7, pp. 58053-58072, 2019.


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  • Canny Edge Detection and Contrast Stretching for Facial Expression Detection and Recognition Using Machine Learning

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Authors

P. Dinesh Kumar
PG and Research Department of Computer Science Engineering, Dr. N.G.P. Arts and Science College, India
B. Rosiline Jeetha
PG and Research Department of Computer Science Engineering, Dr. N.G.P. Arts and Science College, India

Abstract


Facial expression recognition in the world is challenging due to various unconstrained conditions. Although existing facial expression classifiers have been almost perfect for analyzing constrained frontal faces, they fail to perform well on partially occluded faces common in the wild. This paper deals with Facial expression detection and recognition through the Viola-jones algorithm and HCNN using the LSTM method to avoid those challenges in recent work. For face detection, basically, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and it helps to perform the feature selection through the whale optimization algorithm, once after compression and further, it minimizes the feature vector given into the HCNN and LSTM model for efficiently identifying the facial expression. In existing work, the feature extraction stage exact finding of the corner becomes a very difficult task, to solve this issue need to use a corner detection algorithm to enhance the feature extraction (corner points) from the face image. One of the main drawbacks of WOA is that it is not good at exploring the search space. To overcome those issues, the work introduced an improved framework for facial image recognition. In which first edges are detected using the canny edge detection operator. Then improved linear contrast stretching is used for image enhancement. Then in feature extraction, the author proposes Hybrid SIFT with Double δ-LBP (Dδ-LBP) to obtain the features that are illumination and pose independent. For face detection, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and significant features are selected by using self-learning chicken swarm optimization, it minimizes the feature vector is given into the Hybrid HCNN and LSTM model for efficiently identifying the facial expression. Experimental results demonstrate the efficiency of the proposed work in terms of accuracy, precision, recall and f-measure.

Keywords


Viola-Jones Algorithm, Face Detection, Edge Detection, Feature Matching, Artificial Bee Colony, Hybrid Convolutional Neural Network

References