In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch. We propose a novel face-recognition-specialized architecture called GroupFace that utilizes multiple group-aware representations, simultaneously, to improve the quality of the embedding feature. The proposed method provides self-distributed labels that balance the number of samples belonging to each group without additional human annotations, and learns the group-aware representations that can narrow down the search space of the target identity. We prove the effectiveness of the proposed method by showing extensive ablation studies and visualizations. All the components of the proposed method can be trained in an end-to-end manner with a marginal increase of computational complexity. Finally, the proposed method achieves the state-of-the-art results with significant improvements in 1:1 face verification and 1:N face identification tasks on the following public datasets: LFW, YTF, CALFW, CPLFW, CFP, AgeDB-30, MegaFace, IJB-B and IJB-C.

This paper proposes a new method for person search, the task of detecting a specific person exemplified by a query image from a gallery of scene images. Current state-of-the-art techniques in person search demonstrate impressive performance, but are limited in terms of efficiency and scalability since they require multiple models and/or have to re-process gallery images per query. We argue that a concise framework with a single neural network can achieve both of scalability and performance at once. In our framework, the network detects people and extracts their appearance features so that person search is done by finding the person closest to the query in the feature space. For performance, we focus on the quality of the person appearance features: Our network is designed and trained to produce person features that are discriminative, fine-grained, adaptive to appearance variations, and robust against person localization errors. To this end, we design channel attention and part-wise spatial attention modules as well as a loss for learning discriminative features. Our framework outperforms current state of the art on the PRW benchmark even with the concise pipeline based on a single network.

When a face is partially occluded in an image, the existing discriminative or generative methods often do not find facial features. This is due to the limitations of local facial feature detectors and appearance modeling in discriminative and generative methods, respectively. To solve this problem, we propose a new facial feature detection method that hybridizes the discriminative and generative methods. The proposed method consists of an initialization stage and optimization stage. The initialization stage detects the face, estimates the facial pose, and obtains the initial parameter set by locating the pose-specific mean shape on the detected face. The optimization stage obtains the facial features by updating the parameter set using the combined Hessian matrix and gradient vector of shape and appearance errors obtained from two methods. Further, we extend the proposed facial feature detection to face tracking by adding a template face obtained from the previous image frame. In experiments, the proposed method yields more accurate facial feature detection or tracking under heavy occlusions and pose variations than the existing methods.

We constructed a face database POSTECH face database (PF07). PF07 contains the true-color face images of 200 people, 100 men and 100 women, representing 320 various images (5 pose variations times 4 expression variations times 16 illumination variations) per person. All of the people in the database are Korean. We also present the results of face recognition experiments under various conditions using three baseline face recognition algorithms in order to provide an example evaluation protocol on the database. The database is expected to be used to evaluate the algorithm of face recognition for Korean people or for people with systematic variations.

In this paper, we proposes an effective and novel approach to recognize subtle facial expression method which is facial expression deformation. The proposed method deforms subtle facial expressions into corresponding extreme facial expressions. Facial expression deformation processes by extracting subtle motion vector of the predefined feature points and amplifying them. By adding amplified motion vector to Active Appearance Models (AAMs) fitted feature points, the extreme facial expression images is recovered (obtained) by the piece-wise affine warping. After facial expression deformation, we extract the shape and appearance features by projecting deformed facial expression image to the AAM shape and appearance model. We use the multi-class Support Vector Machines (SVMs) to classify the shape and appearance features. The facial expression recognition performance shows promising results of the proposed method.

This paper proposes a new method to speed up the Resolution-Aware Fitting (RAF) algorithm. An interpolation operator is used instead of a blur operator in the RAF algorithm. The RAF algorithm with the interpolation operator is twice as fast as the RAF algorithm with the blur operator without losing fitting accuracy. It concluded that the RAF algorithm with the interpolation operator is superior to the RAF algorithm with the blur operator.