Exploring Lowlevel and Highlevel Transfer Learning for Multitask Facial Recognition with a Semisupervised Neural Network Facial recognition tasks like identity, age, gender, and emotion recognition received substantial attention in recent years. Their deployment in robotic platforms became necessary for the characterization of most of the nonverbal HumanRobot Interaction (HRI) scenarios. In this regard, deep convolution neural networks have shown to be effective on processing different facial representations but with a high cost: to achieve maximum generalization, they require an enormous amount of taskspecific labeled data. This paper proposes a unified semisupervised deep neural model to address this problem. Our hybrid model is composed of an unsupervised deep generative adversarial network which learns fundamental characteristics of facial representations, and a set of convolution channels that finetunes the highlevel facial concepts for the recognition of identity, age group, gender, and facial expressions. Our network employs progressive lateral connections between the convolution channels so that they share the highabstraction particularities of each of these tasks in order to reduce the necessity of a large amount of strongly labeled training data. We propose a series of experiments to evaluate each individual mechanism of our hybrid model, in particular, the impact of the progressive connections on learning the specific facial recognition tasks and we observe that our model achieves a better performance when compared to taskspecific models.

Comparative Convolutional Neural Network for Younger Face Identification We consider the problem of determining whether a pair of face images can be distinguishable in terms of age and if so, which is the younger of the two. We also determine the degree of distinguishability in which age differences are categorized into large, medium, small and tiny. We propose a comparative convolutional neural network combining two parallel deep architectures. Based on the two deep learnt face features, we introduce a comparative layer to represent their mutual relationships, followed by a concatenatation implementation. Softmax is adopted to complete the classification task. To demonstrate our approach, we construct a very large dataset consisting of over 1.7 million face image pairs with youngold labels.