GRNet: An Efficient Group Ranking Network for Face Attribute Recognition Face attribute recognition methods have been far from realworld applications despite remarkable progress in recent years. Most of these methods fail to mine attribute relationships with traditional cross entropy loss and hold considerable computational complexity. In this work, we propose a group ranking network (GRNet) to investigate substantial relationships among face attributes. First, an attribute grouping manner is designed to capture interactions among spatially related attributes and enhance computational efficiency. Second, a new supervision signal is presented to model attribute ranking relationships. Under the supervision of ranking loss, GRNet learns intraattribute and interattribute ranking features to intensify the representational capability of attribute models. We evaluate our approach on aligned and unaligned CelebA datasets. Results show that the performance of the proposed approach is superior to other startoftheart methods on attribute recognition.

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.