Deep learning methods for facial expression recognition

Deep learning is very popular methods for facial expression recognition (FER) and classification. Different types of deep learning algorithms have been used for FER such as deep belief network (DBN) and convolutional neural network (CNN). In this paper, we analyze various deep learning methods and their results. We have chosen Deep convolutional neural network as the best algorithms for facial expression detection and classification. In our study, we have tested the algorithm using Japanese Female facial expressions database (JAFFE) datasets by anaconda software. The deep convolution neural networks with JAFFE datasets accuracy rate around 97.01%

Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields

Automated Facial Expression Recognition (FER) has been a challenging task for decades. Many of the existing works use hand-crafted features such as LBP, HOG, LPQ, and Histogram of Optical Flow (HOF) combined with classifiers such as Support Vector Machines for expression recognition. These methods often require rigorous hyperparameter tuning to achieve good results. Recently Deep Neural Networks (DNN) have shown to outperform traditional methods in visual object recognition. In this paper, we propose a two-part network consisting of a DNN-based architecture followed by a Conditional Random Field (CRF) module for facial expression recognition in videos. The first part captures the spatial relation within facial images using convolutional layers followed by three Inception-ResNet modules and two fully-connected layers. To capture the temporal relation between the image frames, we use linear chain CRF in the second part of our network. We evaluate our proposed network on three publicly available databases, viz. CK+, MMI, and FERA. Experiments are performed in subject-independent and cross-database manners. Our experimental results show that cascading the deep network architecture with the CRF module considerably increases the recognition of facial expressions in videos and in particular it outperforms the state-of-the-art methods in the cross-database experiments and yields comparable results in the subject-independent experiments.Comment: To appear in 12th IEEE Conference on Automatic Face and Gesture Recognition Worksho

Pengenalan Ekspresi Wajah dengan Metode Viola Jones dan Convolutional Neural Network

Currently, the use of artificial intelligence is growing rapidly, including being used to recognize human facial expressions. Human facial expressions have a complex recognition rate. In this study, deep learning will be applied to find out how much accuracy the recognition of facial expressions. The method used in this study is a combination of Viola Jones and Convolutional Neural Network. Viola Jones is used at the segmentation stage and Convolutional Neural Network to classify data. The facial expression dataset that was analyzed consisted of happiness, anger, disgust, sadness, fear, surprise and normal totaling 2205 data. Tests conducted using a conffusion matrix with an accuracy rate of 96.14%. The results of this test indicate that the proposed method has good accuracy for recognizing facial expressions.Saat ini penggunaan kecerdasan buatan berkembang dengan pesat, diantaranya dimanfaatkan untuk mengenali ekspresi wajah manusia. Ekspresi wajah manusia memiliki tingkat pengenalan yang kompleks. Pada penelitian ini akan diterapkan deep learning untuk mengetahui seberapa besar tingkat akurasi dalam pengenalan ekspresi wajah. Metode yang digunakan dalam penelitian ini yaitu gabungan Viola Jones dan Convolutional Neural Network. Viola Jones digunakan pada tahap segmentasi dan Convolutional Neural Network untuk mengklasifikasi data. Dataset ekspresi wajah yang dianalisis terdiri dari bahagia, merah, muak, sedih, takut, terkejut dan normal sejumlah 2205 data. Pengujian yang dilakukan menggunakan confussion matrix dengan tingkat akurasi sebesar 96,14%. Dari hasil pengujian ini menunjukan bahwa metode yang diusulkan memiliki akurasi yang baik untuk mengenali ekspresi wajah

Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories

In this paper, we propose a new approach for facial expression recognition using deep covariance descriptors. The solution is based on the idea of encoding local and global Deep Convolutional Neural Network (DCNN) features extracted from still images, in compact local and global covariance descriptors. The space geometry of the covariance matrices is that of Symmetric Positive Definite (SPD) matrices. By conducting the classification of static facial expressions using Support Vector Machine (SVM) with a valid Gaussian kernel on the SPD manifold, we show that deep covariance descriptors are more effective than the standard classification with fully connected layers and softmax. Besides, we propose a completely new and original solution to model the temporal dynamic of facial expressions as deep trajectories on the SPD manifold. As an extension of the classification pipeline of covariance descriptors, we apply SVM with valid positive definite kernels derived from global alignment for deep covariance trajectories classification. By performing extensive experiments on the Oulu-CASIA, CK+, and SFEW datasets, we show that both the proposed static and dynamic approaches achieve state-of-the-art performance for facial expression recognition outperforming many recent approaches.Comment: A preliminary version of this work appeared in "Otberdout N, Kacem A, Daoudi M, Ballihi L, Berretti S. Deep Covariance Descriptors for Facial Expression Recognition, in British Machine Vision Conference 2018, BMVC 2018, Northumbria University, Newcastle, UK, September 3-6, 2018. ; 2018 :159." arXiv admin note: substantial text overlap with arXiv:1805.0386