Human pose estimation is an interesting and underlying topic in various fields such as action recognition and human-computer interaction. Although many methods have been developed recently, they are still far from perfect in accuracy and speed at a time. In this paper, we propose a Classification-based Pose Estimation Network with Multi-task Learning (CPENML) based on the low-resolution feature map to improve accuracy and inference time simultaneously. The proposed CPENML consists of two ideas. Firstly, novel proposed keypoint and offset estimation tasks based on classification achieve better performance than regression. Secondly, the proposed Multi-Scale Network (MSN) makes robust feature maps and balances the keypoint and offset tasks to maximize performance. To prove the effectiveness of the proposed method, we conduct ablation studies on the COCO dataset for proposed ideas. Compared to benchmarks, we demonstrate the superiority of our proposed method on COCO dataset in terms of inference time and accuracy.

In this article, we focus on decomposing latent representations in generative adversarial networks or learned feature representations in deep autoencoders into semantically controllable factors in a semisupervised manner, without modifying the original trained models. Particularly, we propose factors' decomposer-entangler network (FDEN) that learns to decompose a latent representation into mutually independent factors. Given a latent representation, the proposed framework draws a set of interpretable factors, each aligned to independent factors of variations by minimizing their total correlation in an information-theoretic means. As a plug-in method, we have applied our proposed FDEN to the existing networks of adversarially learned inference and pioneer network and performed computer vision tasks of image-to-image translation in semantic ways, e.g., changing styles, while keeping the identity of a subject, and object classification in a few-shot learning scheme. We have also validated the effectiveness of the proposed method with various ablation studies in the qualitative, quantitative, and statistical examination.

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.

Lately, electroencephalography (EEG)-based auth- entication has received considerable attention from the scientific community. However, the limited usability of wet EEG electrodes as well as low accuracy for large numbers of users have so far prevented this new technology to become commonplace. In this study a novel EEG-based authentication system is presented, which is based on the rapid serial visual presentation paradigm and uses a knowledge-based approach for authentication. Twenty-nine subjects’ data were recorded and analyzed with wet EEG electrodes as well as dry ones. A true acceptance rate of 100% can be reached for all subjects with an average required login time of 13.5 s for wet and 27 s for dry electrodes. Average false acceptance rates for the dry electrode setup were estimated to be$3.33 \times 10^{-5}$.

A similar shader retrieval searches for shaders similar to a given query shader, and significantly reduces trial-and-errors and long processing time in a shading process. However, the developing of similarity measure is quite challenging because of the two characteristics of shader: (1) Shaders have different numbers of attributes that are peculiar to each of them, (2) Since the number of attributes in a shader becomes up to hundreds, the 'dimensionality curse' occurs. In this paper, we propose a novel method for extracting perception feature in effective similar shader retrieval. The proposed method finds low-dimensional features by analyzing hundreds attributes in a shader. The characteristics of extracted features are exactly the same with all the shaders, thereby making the similar shader retrieval much simpler. The proposed method constructs the perception features representing a shader by analyzing the meanings and relationships of all the attributes in the shader. Therefore, the method provides accurate results in the similar shader retrieval. To show the effectiveness of the proposed method, we conduct a variety of experiments.

In this paper we present an approach to robustly align facial features to a face image even when the face is partially occluded. Previous methods are vulnerable to partial occlusion of the face, since it is assumed, explicitly or implicitly, that there is no significant occlusion. In order to cope with this difficulty, our approach relies on two schemes: one is explicit multi-modal representation of the response from each of the face feature detectors, and the other is RANSAC hypothesize-and-test search for the correct alignment over subset samplings of those in the feature response modes. We evaluated the proposed method on a large number of facial images, occluded and non-occluded. The results demonstrated that the alignment is accurate and stable over a wide range of degrees and variations of occlusion.

Recently, many studies on gestural control methods for substituting for keyboard and mouse devices have been conducted because of their conveniences and intuitiveness. This paper presents a Virtual Mouse interface which is a gesture-based mouse interface and Two-layered Bayesian Network (TBN) for robust hand gesture recognition in real-time. The TBN provides robust recognition of hand gestures, as it compensates for an incorrectly recognized hand posture and its location via the preceding and following information. Experiments demonstrate that the proposed model recognizes hand gestures with a recognition rate of 93.78% and 85.15% for a simple and cluttered background, respectively.

In this paper, we present a real-time 3D pointing gesture recognition algorithm for natural human-robot interaction (HRI). The recognition errors in previous pointing gesture recognition algorithms are mainly caused by the low performance of the hands tracking module and by the unreliability of the direction estimate itself, therefore our proposed algorithm uses 3D particle filter for achieving reliability in hand tracking and cascade hidden Markov model (HMM) for a robust estimate for the pointing direction. When someone enters the field of view of the camera, his or her face and two hands are located and tracked using the particle filters. The first stage HMM takes the hand position estimate and maps it to a more accurate position by modeling the kinematic characteristics of finger pointing. The resulting 3D coordinates are used as an input to the second stage HMM that discriminates pointing gestures from others. Finally the pointing direction is estimated in the case of pointing state. The proposed method can deal with both large and small pointing gestures. The experiment shows better than 89% gesture recognition results and 99% target selection results.

Many methods for human gesture recognition have been researched. Bayesian Network (BN) and Dynamic Bayesian Network (DBN) are representative powerful tools for the gesture recognition. However, conventional BN is not appropriate in sequential data, and conventional DBN does not always guarantee that a sequence has relatively higher probability in a true class than in other classes. Moreover, the complexity of the DBN is increased exponentially with increasing number of hidden nodes and large number of training data is needed to guarantee the performance. Therefore, we propose a Semi-DBN (Semi-Dynamic Bayesian Network) which outperforms the conventional BNs and DBNs while it requires much less computational cost.

Human face is one of the most common and useful keys to a person's identity. Although, a number of face recognition algorithms have been proposed, many researchers believe that the technology should be improved further in order to overcome the instability caused by variable illuminations, expressions, poses and accessories. To analyze these face recognition algorithm, it is indispensable to collect various data as much as possible. Face databases such as CMU PIE (USA), FERET (USA), AR Face DB (USA) and XM2VTS (UK) are the representative ones commonly used. However, many databases do not provide adequately annotated information of the pose angle, illumination angle, illumination color and ground-truth. Mostly, they do not include large enough number of images and video data taken under various environments. Furthermore, the faces on these databases have different characteristics from those of Asian. Thus, we have designed and constructed a Korean Face Database (KFDB) which includes not only images but also video clips, ground-truth information of facial feature points and descriptions of subjects and environment conditions so that it can be used for general purposes. In this paper, we present the KFDB which contains image and video data for 1920 subjects and has been constructed in 3 years (sessions). We also present recognition results by CM (Correlation Matching) and PCA (Principal Component Analysis) which are used as baseline algorithms upon CMU PIE and KFDB, so as to understand how recognition rate is changed by altering image taking conditions.