Aff-Wild Database and AffWildNet

In the context of HCI, building an automatic system to recognize affect of human facial expression in real-world condition is very crucial to make machine interact naturallisticaly with a man. However, existing facial emotion databases usually contain expression in the limited scenario under well-controlled condition. Aff-Wild is currently the largest database consisting of spontaneous facial expression in the wild annotated with valence and arousal. The first contribution of this project is the completion of extending Aff-Wild database which is fulfilled by collecting videos from YouTube on which the videos have spontaneous facial expressions in the wild, annotating videos with valence and arousal ranging in [-1,1], detecting faces in frames using FFLD2 detector and partitioning the whole data set into train, validate and test set, with 527056, 94223 and 135145 frames. The diversity is guaranteed regarding age, ethnicity and values of valence and arousal. The ratio of male to female is close to 1. Regarding the techniques used to build the automatic system, deep learning is outstanding since almost all winning methods in emotion challenges adopt DNN techniques. The second contribution of this project is that an end-to-end DNN is constructed to have joint CNN and RNN block and gives the estimation on valence and arousal for each frame in sequential data. VGGFace, ResNet, DenseNet with the corresponding pre-trained model for CNN block and LSTM, GRU, IndRNN, Attention mechanism for RNN block are experimented aiming to find the best combination. Fine tuning and transfer learning techniques are also tried out. By comparing the CCC evaluation value on test data, the best model is found to be pre-trained VGGFace connected with 2 layers GRU with attention mechanism. The models test performance is 0.555 CCC for valence with sequence length 80 and 0.499 CCC for arousal with sequence length 70

Audio-visual Attentive Fusion for Continuous Emotion Recognition

We propose an audio-visual spatial-temporal deep neural network with: (1) a visual block containing a pretrained 2D-CNN followed by a temporal convolutional network (TCN); (2) an aural block containing several parallel TCNs; and (3) a leader-follower attentive fusion block combining the audio-visual information. The TCN with large history coverage enables our model to exploit spatial-temporal information within a much larger window length (i.e., 300) than that from the baseline and state-of-the-art methods (i.e., 36 or 48). The fusion block emphasizes the visual modality while exploits the noisy aural modality using the inter-modality attention mechanism. To make full use of the data and alleviate over-fitting, cross-validation is carried out on the training and validation set. The concordance correlation coefficient (CCC) centering is used to merge the results from each fold. On the development set, the achieved CCC is 0.469 for valence and 0.649 for arousal, which significantly outperforms the baseline method with the corresponding CCC of 0.210 and 0.230 for valence and arousal, respectively. The code is available at https://github.com/sucv/ABAW2.Comment: 6 page