4,561 research outputs found
Island Loss for Learning Discriminative Features in Facial Expression Recognition
Over the past few years, Convolutional Neural Networks (CNNs) have shown
promise on facial expression recognition. However, the performance degrades
dramatically under real-world settings due to variations introduced by subtle
facial appearance changes, head pose variations, illumination changes, and
occlusions.
In this paper, a novel island loss is proposed to enhance the discriminative
power of the deeply learned features. Specifically, the IL is designed to
reduce the intra-class variations while enlarging the inter-class differences
simultaneously. Experimental results on four benchmark expression databases
have demonstrated that the CNN with the proposed island loss (IL-CNN)
outperforms the baseline CNN models with either traditional softmax loss or the
center loss and achieves comparable or better performance compared with the
state-of-the-art methods for facial expression recognition.Comment: 8 pages, 3 figure
Gaussian process domain experts for model adaptation in facial behavior analysis
We present a novel approach for supervised domain adaptation that is based upon the probabilistic framework of Gaussian processes (GPs). Specifically, we introduce domain-specific GPs as local experts for facial expression classification from face images. The adaptation of the classifier is facilitated in probabilistic fashion by conditioning the target expert on multiple source experts. Furthermore, in contrast to existing adaptation approaches, we also learn a target expert from available target data solely. Then, a single and confident classifier is obtained by combining the predictions from multiple experts based on their confidence. Learning of the model is efficient and requires no retraining/reweighting of the source classifiers. We evaluate the proposed approach on two publicly available datasets for multi-class (MultiPIE) and multi-label (DISFA) facial expression classification. To this end, we perform adaptation of two contextual factors: where (view) and who (subject). We show in our experiments that the proposed approach consistently outperforms both source and target classifiers, while using as few as 30 target examples. It also outperforms the state-of-the-art approaches for supervised domain adaptation
DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization
Facial action unit recognition has many applications from market research to
psychotherapy and from image captioning to entertainment. Despite its recent
progress, deployment of these models has been impeded due to their limited
generalization to unseen people and demographics. This work conducts an
in-depth analysis of performance across several dimensions: individuals(40
subjects), genders (male and female), skin types (darker and lighter), and
databases (BP4D and DISFA). To help suppress the variance in data, we use the
notion of self-supervised denoising autoencoders to design a method for deep
face normalization(DeepFN) that transfers facial expressions of different
people onto a common facial template which is then used to train and evaluate
facial action recognition models. We show that person-independent models yield
significantly lower performance (55% average F1 and accuracy across 40
subjects) than person-dependent models (60.3%), leading to a generalization gap
of 5.3%. However, normalizing the data with the newly introduced DeepFN
significantly increased the performance of person-independent models (59.6%),
effectively reducing the gap. Similarly, we observed generalization gaps when
considering gender (2.4%), skin type (5.3%), and dataset (9.4%), which were
significantly reduced with the use of DeepFN. These findings represent an
important step towards the creation of more generalizable facial action unit
recognition systems
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