Learn to synthesize and synthesize to learn

Attribute guided face image synthesis aims to manipulate attributes on a face image. Most existing methods for image-to-image translation can either perform a fixed translation between any two image domains using a single attribute or require training data with the attributes of interest for each subject. Therefore, these methods could only train one specific model for each pair of image domains, which limits their ability in dealing with more than two domains. Another disadvantage of these methods is that they often suffer from the common problem of mode collapse that degrades the quality of the generated images. To overcome these shortcomings, we propose attribute guided face image generation method using a single model, which is capable to synthesize multiple photo-realistic face images conditioned on the attributes of interest. In addition, we adopt the proposed model to increase the realism of the simulated face images while preserving the face characteristics. Compared to existing models, synthetic face images generated by our method present a good photorealistic quality on several face datasets. Finally, we demonstrate that generated facial images can be used for synthetic data augmentation, and improve the performance of the classifier used for facial expression recognition.Comment: Accepted to Computer Vision and Image Understanding (CVIU

Improved Deep Convolutional Neural Network with Age Augmentation for Facial Emotion Recognition in Social Companion Robotics

Facial emotion recognition (FER) is a critical component for affective computing in social companion robotics. Current FER datasets are not sufficiently age-diversified as they are predominantly adults excluding seniors above fifty years of age which is the target group in long-term care facilities. Data collection from this age group is more challenging due to their privacy concerns and also restrictions under pandemic situations such as COVID-19. We address this issue by using age augmentation which could act as a regularizer and reduce the overfitting of the classifier as well. Our comprehensive experiments show that improving a typical Deep Convolutional Neural Network (CNN) architecture with facial age augmentation improves both the accuracy and standard deviation of the classifier when predicting emotions of diverse age groups including seniors. The proposed framework is a promising step towards improving a participant’s experience and interactions with social companion robots with affective computing

Adversarial attack driven data augmentation for medical images

An important stage in medical image analysis is segmentation, which aids in focusing on the required area of an image and speeds up findings. Fortunately, deep learning models have taken over with their high-performing capabilities, making this process simpler. The deep learning model’s reliance on vast data, however, makes it difficult to utilize for medical image analysis due to the scarcity of data samples. Too far, a number of data augmentations techniques have been employed to address the issue of data unavailability. Here, we present a novel method of augmentation that enabled the UNet model to segment the input dataset with about 90% accuracy in just 30 epochs. We describe the us- age of fast gradient sign method (FGSM) as an augmentation tool for adversarial machine learning attack methods. Besides, we have developed the method of Inverse FGSM, which im- proves performance by operating in the opposite way from FGSM adversarial attacks. In comparison to the conventional FGSM methodology, our strategy boosted performance up to 6% to 7% on average. The model became more resilient to hostile attacks because to these two strategies. An innovative implementation of adversarial machine learning and resilience augmentation is revealed by the overall analysis of this study

Data Augmentation Using Generative Adversarial Techniques for Emotion Classification

In classification of a small number of labeled examples with multiple labels, it is hard to get high accuracy in convolutional neural network if the category distribution is imbalanced. Especially for facial emotional expressions dataset, some kinds of facial emotional expression data are often lacking, however, neutral facial emotional expression data are abundant. Data augmentation is an effective way to improve generalization ability of models and greatly enrich the data distribution of samples. Many famous generative adversarial models such as Pix2Pix can implement the task of data augmentation. Pix2Pix solves the problem of image generation from one domain to another domain successfully. However, for some special dataset, such as facial emotional expression dataset, only one category of data is relatively easy to obtain, while other categories of data are relatively smaller. In this case, multi-domain model StarGAN can perform well over the time.In this paper, we propose a data augmentation approach using generative adversarial technique StarGAN. We generate other seven categories of augmented data using neutral facial emotional expressions in StarGAN model. In two scenarios, where only one category are relatively lacking in eight categories including neutral facial emotional expression and the number of other seven categories are relatively smaller than neutral emotional expression, the results are evaluated using VGG-16 model. Compared with some conventional methods such as rotation and crop, the accuracy of emotional expression classification is enhanced using data augmentation of StarGAN method in both two situations. We confirm that data augmentation using generative adversarial techniques are more effective than other conventional approaches