Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation.

Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise.

Multi-Granularity Aggregation with Spatiotemporal Consistency for Video-Based Person Re-Identification.

Video-based person re-identification (ReID) aims to exploit relevant features from spatial and temporal knowledge. Widely used methods include the part- and attention-based approaches for suppressing irrelevant spatial-temporal features. However, it is still challenging to overcome inconsistencies across video frames due to occlusion and imperfect detection.

Determination of Luteolin 7-Glucuronide in (L.) Britt. Leaf Extracts from Different Regions of China and Republic of Korea and Its Cholesterol-Lowering Effect.

Lowering blood cholesterol levels is crucial for reducing the risk of cardiovascular disease in patients with familial hypercholesterolemia. To develop (L.) Britt.

Screening and Evaluation of Active Compounds in Polyphenol Mixtures by a Novel AAPH Offline HPLC Method and Its Application.

In this study, we developed a novel offline high-performance liquid chromatography (HPLC) method based on 2,2'-azobis(2-amidinopropane) dihydrochloride (AAPH) radicals for antioxidant screening in 20 polyphenolic compounds and used the Trolox equivalent antioxidant capacity assay to evaluate their antioxidant activity. Compared to the existing offline HPLC methods based on 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH), the offline HPLC method based on the AAPH radical is more sensitive. Additionally, we applied this method to (Walp.

Geometry-Aware Deep Video Deblurring via Recurrent Feature Refinement.

Blurring in videos is a frequent phenomenon in real-world video data owing to camera shake or object movement at different scene depths. Hence, video deblurring is an ill-posed problem that requires understanding of geometric and temporal information. Traditional model-based optimization methods first define a degradation model and then solve an optimization problem to recover the latent frames with a variational model for additional external information, such as optical flow, segmentation, depth, or camera movement.

Enhanced Standard Compatible Image Compression Framework Based on Auxiliary Codec Networks.

Recent deep neural network-based research to enhance image compression performance can be divided into three categories: learnable codecs, postprocessing networks, and compact representation networks. The learnable codec has been designed for end-to-end learning beyond the conventional compression modules. The postprocessing network increases the quality of decoded images using example-based learning.

An Efficient Approach Using Knowledge Distillation Methods to Stabilize Performance in a Lightweight Top-Down Posture Estimation Network.

Multi-person pose estimation has been gaining considerable interest due to its use in several real-world applications, such as activity recognition, motion capture, and augmented reality. Although the improvement of the accuracy and speed of multi-person pose estimation techniques has been recently studied, limitations still exist in balancing these two aspects. In this paper, a novel knowledge distilled lightweight top-down pose network (KDLPN) is proposed that balances computational complexity and accuracy.

Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information.

In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features.

L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification.

The decision tree is one of the most effective tools for deriving meaningful outcomes from image data acquired from the visual sensors. Owing to its reliability, superior generalization abilities, and easy implementation, the tree model has been widely used in various applications. However, in image classification problems, conventional tree methods use only a few sparse attributes as the splitting criterion.

Efficient Depth Enhancement Using a Combination of Color and Depth Information.

Studies on depth images containing three-dimensional information have been performed for many practical applications. However, the depth images acquired from depth sensors have inherent problems, such as missing values and noisy boundaries. These problems significantly affect the performance of applications that use a depth image as their input.

Re-adjusting the cut-off score of the Korean version of the Childhood Autism Rating Scale for high-functioning individuals with autism spectrum disorder.

Aim: The current cut-off score of the Korean version of the Childhood Autism Rating Scale (K-CARS) does not seem to be sensitive enough to precisely diagnose high-functioning autism. The aim of this study was to identify the optimal cut-off score of K-CARS for diagnosing high-functioning individuals with autism spectrum disorders (ASD).

Methods: A total of 329 participants were assessed by the Korean versions of the Autism Diagnostic Interview - Revised (K-ADI-R), Autism Diagnostic Observation Schedule (K-ADOS), and K-CARS.

Tracking and Classification of In-Air Hand Gesture Based on Thermal Guided Joint Filter.

The research on hand gestures has attracted many image processing-related studies, as it intuitively conveys the intention of a human as it pertains to motional meaning. Various sensors have been used to exploit the advantages of different modalities for the extraction of important information conveyed by the hand gesture of a user. Although many works have focused on learning the benefits of thermal information from thermal cameras, most have focused on face recognition or human body detection, rather than hand gesture recognition.

Face liveness detection using defocus.

In order to develop security systems for identity authentication, face recognition (FR) technology has been applied. One of the main problems of applying FR technology is that the systems are especially vulnerable to attacks with spoofing faces (e.g.

Depth camera-based 3D hand gesture controls with immersive tactile feedback for natural mid-air gesture interactions.

Vision-based hand gesture interactions are natural and intuitive when interacting with computers, since we naturally exploit gestures to communicate with other people. However, it is agreed that users suffer from discomfort and fatigue when using gesture-controlled interfaces, due to the lack of physical feedback. To solve the problem, we propose a novel complete solution of a hand gesture control system employing immersive tactile feedback to the user's hand.

Face liveness detection using a light field camera.

A light field camera is a sensor that can record the directions as well as the colors of incident rays. This camera is widely utilized from 3D reconstruction to face and iris recognition. In this paper, we suggest a novel approach for defending spoofing face attacks, like printed 2D facial photos (hereinafter 2D photos) and HD tablet images, using the light field camera.

Stereo camera based virtual cane system with identifiable distance tactile feedback for the blind.

In this paper, we propose a new haptic-assisted virtual cane system operated by a simple finger pointing gesture. The system is developed by two stages: development of visual information delivery assistant (VIDA) with a stereo camera and adding a tactile feedback interface with dual actuators for guidance and distance feedbacks. In the first stage, user's pointing finger is automatically detected using color and disparity data from stereo images and then a 3D pointing direction of the finger is estimated with its geometric and textural features.

Random-profiles-based 3D face recognition system.

In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention.

3D multi-spectrum sensor system with face recognition.

This paper presents a novel three-dimensional (3D) multi-spectrum sensor system, which combines a 3D depth sensor and multiple optical sensors for different wavelengths. Various image sensors, such as visible, infrared (IR) and 3D sensors, have been introduced into the commercial market. Since each sensor has its own advantages under various environmental conditions, the performance of an application depends highly on selecting the correct sensor or combination of sensors.

Effects of electron transport material on blue organ light-emitting diode with fluorescent dopant of BCzVBi.

High efficiency blue organic light emitting diodes (OLEDs), based on 2-me-thyl-9,10-di(2-naphthyl) anthracene (MADN) doped with 4,4'-bis(9-ethyl-3-carbazovinylene)-1,1'-biphenyl (BCzVBi), were fabricated using two different electron transport layers (ETLs) of tris(8-hydroxyquinolino)-aluminum (Alq3) and 4,7-di-phenyl-1,10-phenanthroline (Bphen). Bphen ETL layers favored the efficient hole-electron recombination in the emissive layer of the BCzVBi-doped blue OLEDs, leading to high luminous efficiency and quantum efficiency of 8.34 cd/A at 100 mA/cm2 and 5.

3D Face modeling using the multi-deformable method.

In this paper, we focus on the problem of the accuracy performance of 3D face modeling techniques using corresponding features in multiple views, which is quite sensitive to feature extraction errors. To solve the problem, we adopt a statistical model-based 3D face modeling approach in a mirror system consisting of two mirrors and a camera. The overall procedure of our 3D facial modeling method has two primary steps: 3D facial shape estimation using a multiple 3D face deformable model and texture mapping using seamless cloning that is a type of gradient-domain blending.

Fingerprint image mosaicking by recursive ridge mapping.

To obtain a large fingerprint image from several small partial images, mosaicking of fingerprint images has been recently researched. However, existing approaches cannot provide accurate transformations for mosaics when it comes to aligning images because of the plastic distortion that may occur due to the nonuniform contact between a finger and a sensor or the deficiency of the correspondences in the images. In this paper, we propose a new scheme for mosaicking fingerprint images, which iteratively matches ridges to overcome the deficiency of the correspondences and compensates for the amount of plastic distortion between two partial images by using a thin-plate spline model.

Alignment-free cancelable fingerprint templates based on local minutiae information.

To replace compromised biometric templates, cancelable biometrics has recently been introduced. The concept is to transform a biometric signal or feature into a new one for enrollment and matching. For making cancelable fingerprint templates, previous approaches used either the relative position of a minutia to a core point or the absolute position of a minutia in a given fingerprint image.

Three-dimensional sensor-based face recognition.

We describe a face recognition system based on two different three-dimensional (3D) sensors. We use 3D sensors to overcome the pose-variation problems that cannot be effectively solved in two-dimensional images. We acquire input data based on a structured-light system and compare it with 3D faces that are obtained from a 3D laser scanner.