Fear Recognition Using Early Biologically Inspired Features Model
محورهای موضوعی : Image Processing
1 - Department of Computer Engineering, Fouman and Shaft Branch, Islamic Azad University, Fouman, Iran
کلید واژه: Fear Recognition, Early Biological model, Support Vector Machine, Facial Expressions,
چکیده مقاله :
Facial expressions determine the inner emotional states of people. Different emotional states such as anger, fear, happiness, etc. can be recognized on people's faces. One of the most important emotional states is the state of fear because it is used to diagnose many diseases such as panic syndrome, post-traumatic stress disorder, etc. The face is one of the biometrics that has been proposed to detect fear because it contains small features that increase the recognition rate. In this paper, a biological model inspired an early biological model is proposed to extract effective features for optimal fear detection. This model is inspired by the model of the brain and nervous system involved with the human brain, so it shows a similar function compare to brain. In this model, four computational layers were used. In the first layer, the input images will be pyramidal in six scales from large to small. Then the whole pyramid entered the next layer and Gabor filter was applied for each image and the results entered the next layer. In the third layer, a later reduction in feature extraction is performed. In the last layer, normalization will be done on the images. Finally, the outputs of the model are given to the svm classifier to perform the recognition operation. Experiments will be performed on JAFFE database images. In the experimental results, it can be seen that the proposed model shows better performance compared to other competing models such as BEL and Naive Bayes model with recognition accuracy, precision and recall of 99.33%, 99.71% and 99.5%, respectively
Facial expressions determine the inner emotional states of people. Different emotional states such as anger, fear, happiness, etc. can be recognized on people's faces. One of the most important emotional states is the state of fear because it is used to diagnose many diseases such as panic syndrome, post-traumatic stress disorder, etc. The face is one of the biometrics that has been proposed to detect fear because it contains small features that increase the recognition rate. In this paper, a biological model inspired an early biological model is proposed to extract effective features for optimal fear detection. This model is inspired by the model of the brain and nervous system involved with the human brain, so it shows a similar function compare to brain. In this model, four computational layers were used. In the first layer, the input images will be pyramidal in six scales from large to small. Then the whole pyramid entered the next layer and Gabor filter was applied for each image and the results entered the next layer. In the third layer, a later reduction in feature extraction is performed. In the last layer, normalization will be done on the images. Finally, the outputs of the model are given to the svm classifier to perform the recognition operation. Experiments will be performed on JAFFE database images. In the experimental results, it can be seen that the proposed model shows better performance compared to other competing models such as BEL and Naive Bayes model with recognition accuracy, precision and recall of 99.33%, 99.71% and 99.5%, respectively
[1] H Ganji, “General Psychology”, Tehran:Savalan, Vol.350, 1386.(Persian).
[2] F Lotfi Kashani, SH Vaziri, “Child's Pathological Psychology”, Tehran:Arasbaran, Vol.344, 1395.(Persian).
[3] Karlson N, Kalat J, Bridola M, N Vatson, M Rosenzevig, “Physiological Psychology: An Introduction to Behavioral”, Cognitive, and Clinical Neuroscience, Tehran:Arasbaran, Vol.468, 1394.(Persian).
[4] J Ledoux, “The Emotional Brain, Fear, and the Amygdala”, Cellular and Molcular Neurobioilogy. Vol.23, 2003, pp.727-738.
[5] J Lin, J Zheng, “Modulating Amygdala–Hippocampal Network Communication: A Potential Therapy for Neuropsychiatric Disorders”, Neuropsychopharmacology. Vol.43, 2018, pp.218-219.
[6] A Fallah, “Fear of Nervous Attacks and Earthquakes”, Daneshmand, Vol.53(9), 1394, pp.56-60.(Persian).
[7] M Miyahara, T Harada, T Ruffman, N Sadato, T Iidaka, “Functional connectivity between amygdala -and facial regions involved in recognition of facial threat”, Soc Cogn Affect Neurosci, Vol.8(2), 2013, pp.181-189.
[8] J Lin, J zheng, “Modulating Amygdala–Hippocampal Network Communication: A Potential Therapy for Neuropsychiatric Disorders”, Neuropsychopharmacol, Vol.43, 2018, pp.218–219.
[9] M Kim, J Shin, J Taylor, A Mattek, S Chavez, P Whalen, “Intolerance of Uncertainty Predicts Increased Striatal Volume”, Emotion, Vol.12(6), 2017, pp.895-899.
[10] H Yongzhen, H Kaiqi, W Liangsheng, T Dacheng, T Tieniu, L Xuelong, “Enhanced Biologically Inspired Model”, Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, 2008, Anchorage, AK, USA.
[11] Fyaghouti, S motamed, “Recognition of Facial Expression of Emotions Based on Brain Emotional Learning (BEL) Model”, Advances in Cognitive Science, Vol.20(4), 2019, pp.46-61.
[12] T Serre, L Wolf, T Poggio, “Object recognition with features inspired by visual cortex”, Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, San Diego, CA, USA.
[13] J G Mutch, D Lowe, “Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields”, International Journal of Computer Vision, Vol.80(1), 2008, PP.45–57.
[14] J Morén J, C Balkenius, “A computational model of emotional learning in the amygdala”, From animals to animats, Vol.6, 2006, PP.115-124.
[15] J Morén J, “Emotion and learning- a computational model of the Amygdala [Ph.D. Dissertation]’, Lund, Sweden: Lund University; 2002.
[16] E Lotfi, “Mathematical modeling of emotional brain for classification problems”, Proceeding of IAM, 2013 January, Vol. 2(1), PP.60-71.
[17] M Wegrzyn, M Vogt, B Kireclioglu, J Schneider, J Kissler, “Mapping the emotional face. How individual face parts contribute to successful emotion recognition”, PLOS ONE, Vol.12(5), 2017.
[18] Y Sima, J Yi, A Chen, Z Jin, “Automatic expression recognition of face image sequence based on key-frame generation and differential emotion feature”, Applied Soft Computing, Vol.113, 2021.
[19] R Campbell, K Elgar, J Kuntsi, R Akers, J Terstegge, M Coleman, D Skuse, The classification of 'fear' from faces is associated with face recognition skill in women”, Neuropsychologia, Vol.40(6), 2002.
[20] M Rinck, M A. Primbs, I A. M. Verpaalen, G Bijlstra, “Face masks impair facial emotion recognition and induce specifc emotion confusions”, Cognitive Research: Principles and Implications, Vol. 83, 2022.
[21] W Mellouk, W Handouzi, “Facial emotion recognition using deep learning: review and insights” Proceeding of the 2nd international Workshop on the Future of Internet of Everything (FIoE), 2020 August 9-12, Leuven, Belgium.
[22] D H Kim, W J Baddar, J Jang, Y M Ro, “Multi-Objective Based Spatio-Temporal Feature Representation Learning Robust to Expression Intensity Variations for Facial Expression Recognition”, IEEE Trans. Affect. Comput, Vol. 10(2), 2019, pp.223 236.
[23] Z Yu, G Liu, Q Liu, J Deng, “Spatio-temporal convolutional features with nested LSTM for facial expression recognition”, Neurocomputing, Vol. 317, 2018, pp. 50 57.
[24] D Liang, H Liang, Z Yu, Y Zhang, “Deep convolutional BiLSTM fusion network for facial expression recognition”, Vis. Comput, Vol. 36(3), pp. 499 508.
[25] S Meshgini, A Aghagolzadeh, H Seyedarabi, “Face recognition using gabor-based direct linear discriminant analysis and support vector machine”, Computers & Electrical Engineering, Vol.39, 2013, pp.727–745.
[26] C Liu, H Wechsler, “Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition”, IEEE Transactions on Image Processing, Vol.11, 2002, pp.467-476.
[27] J K Kamarainen, V Kyrki, H Kalviainen, “Invariance properties of gabor filter-based features-overview and applications”, IEEE Transactions on Image Processing, Vol.15, 2006, pp.1088-1099.
[28] L Shen, L Bai, M Fairhurst, “Gabor wavelets and general discriminant analysis for face identification and verification”, Image and Vision Computing, Vol.25, 2006, pp.553-563.
[29] J G Daugman, “Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression”, IEEE Transactions on acoustics, speech, and signal processing, Vol.36(7), 1988, pp.1169-1179.
[30] Y-L Boureau, J Ponce, Y LeCun, “A theoretical analysis of feature pooling in visual recognition”, Proceedings of the 27th international conference on machine learning (ICML-10), 2010, Haifa, Israel.
[31] S M Tabatabaii, T M Nazeri, M Dastorani, “Archive of SID Performance comparison of GP, ANN, BCSD and SVM models for temperature simulation”, Journal of Meteorology and Atmospheric Sciences, Vol.1(1), 2018, pp.53-64. (Persian).
[32] N Sebe, M.S Lew, I Cohen, A Garg, T.S Huang, “Emotion recognition using a Cauchy Naive Bayes classifier”, Proceeding of 16th International Conference on Pattern Recognition, 2002 August.
