Facial Emotion Recognition using Deep Learning
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1 Indian Institute of Technology Kanpur Facial Emotion Recognition using Deep Learning Ankit Awasthi (Y8084) CS 676:Computer Vision Supervisor: Dr. Amitabha Mukerjee, Department of Computer Science Engineering, IIT Kanpur Dr. P Guha, TCS Labs, Delhi,India
2 ABSTRACT Facial emotion recognition is one of the most important cognitive functions that our brain performs quite efficiently. State of the art facial emotion recognition techniques are mostly performance driven and do not consider the cognitive relevance of the model. This project is an attempt to look at the task of emotion recognition using deep belief networks which is cognitively very appealing and at the same has been shown to perform very well for digit recognition (Hinton et.al. 2006). We look at the effects of varying number of hidden layers and hidden units on the performance of the model and attempt to develop important insights into the features learnt by the model. Also we observe that as found various psychological findings our model finds lower spatial frequency more useful for recognizing facial expressions than higher spatial frequency data. 1
3 Contents 1 Introduction 3 2 Motivation 4 3 Restricted Boltzmann Machine 4 4 Deep Belief Networks 5 5 JAFFE Dataset 5 6 Results First Hidden Layer Features Effect of Number of Layers Effect of Number of Hidden Units Effect of Image Size Discussion and Future Work 10 8 References 11 2
4 1 Introduction Facial expression are important cues for non verbal communication among human beings. This is only possible because humans are able to recognize emotions quite accurately and efficiently. An automatic facial emotion recognition system is an important component in human machine interaction. Apart from the commercial uses of an automatic facial emotion recognition system it might be useful to incorporate some cues from the biological system in the model and use the model to develop further insights into the cognitive processing of our brain. State of the art approaches in facial emotion recognition use Active Appearance Models(AAMs), FACS labels or some other sophisticated feature extraction scheme. AAMs can be learned from a set of training images and can be fitted on a new face to generate the landmark positions which can further be used to design features. Thus, in an automatic setting either the availability of landmark point on face images is assumed or can be obtained by fitting the model. FACS labels attempt to decompose human emotions in terms of Action Units(AUs) which correspond to specific muscle movements. FACS coding system is used in psychology and animation to classify facial expressions in a consistent and systematic manner.but as of now FACS labels can only be given by experts or trained individuals. One problem with ad hoc feature extraction schemes is that we need to design separate feature extraction mechanism foe each visual task to be perfomed. Moreover,it is known that only some of the filters in the retina are hardcoded and the other units in the V1,V2 and higher areas of visual processing are learned.hubel and Wiesel showed that irreversible damage was produced in kittens by sufficient visual deprivation during the so called critical period. Therefore,it makes much more sense to have generic scheme for learning what transformations in the input space may lead to good features for performing a particular task. There is ample evidence that our visual processing architecture is organized in different levels. Each level transforms the input in a manner that facilitates the visual task to be performed. Another appealing feature of deep learning models is that there can be feature or sub-feature sharing. Computationally also, it has been shown that unsufficiently deep architectures can be exponentially ineffiecient. Deep Learning was revolutionized by Hinton et.al.[1] when they came up with a very efficient method for training multilayer neural networks. 3
5 2 Motivation Deep Learning methods have performed very well in MNSIT digit recognition dataset[1]. Our setting is very similar to the task of digit recognition. Corresponding to the digit labels we have emotion labels. But emotion recognition is much more complicated because digit images are much simpler than face images depicting various expressions. Moreover the variability in the images due to different identities hampers the performance. Human accuracy in facial exression recognition is not as good as in digit recognition and is also aided by other modes of information such as context,prior experience,speech among others. 3 Restricted Boltzmann Machine The restricted Boltzmann machine(rbm) is a two-layer, undirected graphical model in which there are no lateral connections. One layer of nodes is called the visible layer v,and the other layer of nodes is called the hidden layer h. Each of these nodes are stochastic binary units and each configuration of visible and hidden nodes is characterized by a energy which is given by the following function E(v, h) = i,j v iw ij h j j b jh j i c iv i Probabilistically,this is interprated as follows: P (v, h) = exp( E(v,h) Z If the visible units are real, energy function is defined as follows E(v, h) = 1 2 i v2 i i,j v iw ij h j j b jh j i c iv i The hidden nodes are conditionally independent given the visible layer and vice versa. In particular, the conditional probabilities are as follows P (v h; θ) = Π i p(v i h), P (h v; θ) = Π j p(h j v) p(h j = 1 v) = sigmoid( i W ijv i + b j ) For binary visible layer, p(v j = 1 h) = sigmoid( i W ijh i + c j ) p(h j = 1 v) = sigmoid( i W ijv i + b j ) For real valued visible layer, we have, p(v j = 1 h) = N( i W ijh i + c j, sigma) The parameters of the RBM can be learned by maximizing the log-likelihood of training 4
6 data using gradient ascent. But the exact gradient of the log-likelihood is intractable,thus contrastive divergence is used which works fairly well in practice.the exact gradient is intractable which is approximated by exact gradient : logp(v) W ij CD approximation: logp(v) W ij = < v i h j > 0 < v i h j > = < v i h j > 0 < v i h j > n 4 Deep Belief Networks RBMs are only intersting because they can be efficiently stacked up layer by layer to form a deep network. First an RBM is trained on the visible layer.once trained the weights are frozen and the hidden layer activations act as the input for the next RBM. Thus a DBN with any number of layers can be formed by stacking RBMs as mentoined above. It has also been shown [1] that increasing the number improves the variational lower bound on the probability of the training data. RBM acts as a fundamental unit in the whole DBN. There are other models that can be used instead of RBMs such autoencoders(sparse),denoising autoencoders.details about sparse autoencoder can be found in [5]. Once the layer by layer model has been trained a final supervised fine tuning step which adjusts the weights to improve the performance on the particular task in hand 5 JAFFE Dataset Japanese Female Facial Expression (JAFFE) Database - The database contains 213 images of 7 facial expressions (6 basic facial expressions + 1 neutral) posed by 10 Japanese female models. Each image has been rated on 6 emotion adjectives by 60 Japanese subjects. Some of the emotions (fear) have been reported to not have been expressed very well. But in this project we are working with all the six emotions rather a reduced set of emotions. 6 Results In this project, we experimented with a lot of different settings of the model hyperarameters to find how they affect the performance. Few variants to the conventionall DBNs were tried such as sparse DBNs and stacking up sparse autoencoders but the results did not show any improvement and hence corresponding results have not been reported. In all the results, the models were trained using 150 training images and tested on the remaining 63 images. 5
7 Figure 1: features learned by the first layer of DBN, image size: 24 x 24, hidden layer: 50 units Deep Belief Networks typically require large amount of data but in our case we have only 213 images. Thus the results may change significantly if a larger dataset is used.the experiments were performed at three resolutions: 100 X 100, 50 X 50,25 X 25. The results for various experiments are stated as follows and would be discussed in the next section. 6.1 First Hidden Layer Features Figure 1, 2, 3 show the features learned by the first hidden layer. It can be observed the when the images are large it is difficult to the features learned are not of good quality. Moreover the number of epochs required to get any meaningful features increases with the image size. Although there is no quantitative way of discriminating between these features other than the recognition task itself (which is also an indirect method), visually the features for smaller image sizes appear to be better than that in case of bigger image size. Projection of higher layer features on the input space is a non-trivial task and has not been dealt with here. 6.2 Effect of Number of Layers Figure 4 shows how the performance varies over the number of epochs of supervised fine tuning step for 24x24 image size. Figure 5, 6 show the performance for image sizes 50x50 and 100x100 respectively. As shown in the figures, increase in the number of layers resulted 6
8 Figure 2: features learned by the first layer of DBN, image size: 50 x 50,hidden layer: 100 units Figure 3: features learned by the first layer of DBN, image size:100 x 100,hidden layer: 500 units 7
9 Figure 4: Performance of DBNs on 24 x 24 images against numner of epochs of supervised finetuning in slight improvement. It was also observed that further increasing the number of layers deteriorates the performance. Results for such cases have not been reported. One possible explaination for the same could be that increasing the number of parameters to be learned and with the small dataset that we have it is difficult to learn many parameters. 6.3 Effect of Number of Hidden Units This part was not exhaustively investigated through experiments, but in general the results have been reported for the best configuration possible for the specified number of hidden layers. One important observation was that it was important to have a sufficient reduction in information from the visible layer to the first hidden layer. In other the number of hidden units in the first layer should be significantly less than the number of visible units. This forces the model to learn important features from the image. 6.4 Effect of Image Size As shown in Figure 4, 5, 6 the performance improves when we move from high resolution images to low resolution images. This complies with psychological findings that lower spatial frequency band are favoured for facial expression recogntion and speaks of the cognitive grounding of our model. However, based on such a small dataset, we refrain from making any claims and the observed phenomenon may be solely because of the fewer parameters to train in case of smaller images. 8
10 Figure 5: Performance of DBNs on 50 x 50 images against numner of epochs of supervised finetuning Figure 6: Performance of DBNs on 100 x 100 images against numner of epochs of supervised finetuning 9
11 7 Discussion and Future Work Accuracy of state of state of the art facial emotion recognition systems is much better than arrived at in the project. Considering that the algorithm takes raw images rather landmark points or FACS labels as input, it performs fairly well. The dataset used in the project was quite small and prohibits any general claim about the success or failure of deep learning methods. It is expected that a larger dataset would improve the accuracy of the algorithm and better features would be learned. This comprises a major portion of our future work in this project. Observing the features one may say that algorithm is able to extract some meaningful features. In the absence of any principled way of discrminating the receptive fields learned by the model it becomes difficult to argue about the goodness or badness of a feature other than evaluating the classfication accuracy that the feature facilitates. As observed increasing number of hidden layers resulted in a slight improvement in classification, but further increase in hidden layers however deteriorated the results. The number of hidden units in each layer was one of the hyperparameters which wasnt satisfactorily investigated but an important and somewhat counter-intuitive observation that came up was that the number of hidden units in the first layer should be less than the number of visible units which in other words means that there should be a significant redcution in the amount of information from the visible layer to the first hidden layer. This is appealing because soemhing very similar happens in our visul system where a lot of information is thrown out in successive layers of processing. What this does is that it forces the hidden units to learn the most important features. Led by this observation,we thought that sparsity constraints might lead to even better features and accuracy but as it turned out that there was not any improvement. Again, this might be attributed to the small dataset we are working with. One of the imortant results coming out of this project is the observation that low resolution images had better classification accuracy than higher resolution images. Various psychological experiments done on human beings suggest that we make use of mid spatial frequency band for recognizing emotions rather than thehigh spatial frequency band. Although here,we do not present any quantitative similarities for spatial frequency versus classification accuracy, the few experiments that we performed suggest that lower spatial frequency information is more useful for recognizing emotions which speaks for the cognitive relevance of the model.in our future work we would like to work quantitative ways of evaluating cognitive imporatnce of features which would help argue for DBNs as a very good model of our visual system. 10
12 8 References References [1] Geoffrey E. Hinton, Yee-Whye Teh and Simon Osindero, A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, pages , Volume 18, [2] Susskind, J.M. and Hinton, G.E. and Movellan, J.R. and Anderson, A.K., Generating facial expressions with deep belief nets, Affective Computing, Emotion Modelling, Synthesis and Recognition, pages ,2009 [3] Michael J. Lyons, Shigeru Akamatsu, Miyuki Kamachi & Jiro Gyoba, Coding Facial Expressions with Gabor Wavelets,Proceedings, Third IEEE International Conference on Automatic Face and Gesture Recognition,pp , 1-19.April [4] Geoffrey E. Hinton (2010). A Practical Guide to Training Restricted Boltzmann Machines,Technical Report,Volume 1 [5] Andrew Ng. (2010). Sparse autoencoder(lecture notes). [6] Honglak Lee, Chaitanya Ekanadham, Andrew Y. Ng, Sparse deep belief net model for visual area V2 NIPS,
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