Publication Type : Conference Paper
Publisher : 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference.
Source : 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on (2018)
Keywords : 2D correlation, 2D Haar wavelet coefficients, Affective Computing, Amritaemo database, Artificial neural networks, Correlation, Databases, Discrete Wavelet Transform, Discrete wavelet transforms, DWT, DWT based difference features, DWT based similarity features, Ekman model, Emotion recognition, emotional images, face, Face recognition, facial emotion recognition, facial images, Feature classification, Feature extraction, Feature parameters, Haar transforms, histogram equalization, image classification, Image filtering, Indian ethnicity, Indian female subjects, Indian male subjects, k-nearest neighbor, learning (artificial intelligence), median filtering, Median filters, nearest neighbour methods, neural nets, neutral images, squared difference, Supervised learning, supervised learning methods, Two dimensional displays
Campus : Amritapuri
School : School of Engineering
Department : Electronics and Communication
Year : 2018
Abstract : Recognizing emotions from facial images has become one of the major fields in affective computing arena since it has wide spread applications in robotics, medicine, surveillance, defense, e-learning, gaming, customer services etc. The study used Ekman model with 7 basic emotions- anger, happy, disgust, sad, fear, surprise and neutral acquired from subjects of Indian ethnicity. The acquired data base, Amritaemo consisted of 700 still images of Indian male and female subjects in seven emotions. The images were then cropped manually to obtain the region of analysis i.e. the face and converted to grayscale for further processing. Preprocessing techniques, histogram equalization and median filtering were applied to these after resizing. Discrete Wavelet Transform (DWT) was applied to these pre-processed images. The 2 D Haar wavelet coefficients (WC) were used to obtain the feature parameters. The maximum 2D correlation of mean value of one specific emotion versus all others was considered as the similarity feature. The squared difference of the emotional and neutral images in the transformed domain was considered as the difference feature. Supervised learning methods, K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANN) were used to classify these features separately as well as together. The performance of these parameters were evaluated based on the measures accuracy, sensitivity and specificity.
Cite this Research Publication : Poorna S. S., Anjana, S., Varma, P., Sajeev, A., Arya, K. C., Renjith, S., and Nair, G. J., “Facial Emotion Recognition using DWT based Similarity and Difference Features”, in 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, 2018