Appearance-Based Gaze Estimation for ASD Diagnosis

Automatic classification of ASD children using appearance-based features from videos

Variational Abnormal Behavior Detection With Motion Consistency

Classifying ASD children with LSTM based on raw videos

Attention mechanism-based CNN for facial expression recognition

Cross-Level Parallel Network for Crowd Counting

Real-time face recognition based on pre-identification and multi-scale classification

Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

With the dramatic expansion of large-scale videos, traditional centralized face recognition methods cannot meet the demands of time efficiency and expansibility, and thus distributed face recognition models were proposed. However, the number of tasks at the agent side is always dynamic, and unbalanced allocation will lead to time delay and a sharp increase of CPU utilization. To this end, a new distributed face recognition framework based on load balancing and dynamic prediction is proposed in this paper. The framework consists of a server and multiple agents. When performing face recognition, the server is used to recognize faces, and other operations are performed by the agents. Since the changes of the total number of videos and the number of pedestrians affect the task amount, we perform load balancing with an improved genetic algorithm. To ensure the accuracy of task allocation, we use extreme learning machine to predict the change of tasks. The server then performs task allocation based on the predicted results sent by the agents. The experimental results show that the proposed method can effectively solve the problem of unbalanced task allocation at the agent side, and meanwhile alleviate time delay and the sharp increase of CPU utilization.

Crowd Counting Via Residual Building Block Convolutional Neural Network

CNN-Based Facial Expression Recognition from Annotated RGB-D Images for Human–Robot Interaction

Facial expression recognition has been widely used in human computer interaction (HCI) systems. Over the years, researchers have proposed different feature descriptors, implemented different classification methods, and carried out a number of experiments on various datasets for automatic facial expression recognition. However, most of them used 2D static images or 2D video sequences for the recognition task. The main limitations of 2D-based analysis are problems associated with variations in pose and illumination, which reduce the recognition accuracy. Therefore, an alternative way is to incorporate depth information acquired by 3D sensor, because it is invariant in both pose and illumination. In this paper, we present a two-stream convolutional neural network (CNN)-based facial expression recognition system and test it on our own RGB-D facial expression dataset collected by Microsoft Kinect for XBOX in unspontaneous scenarios since Kinect is an inexpensive and portable device to capture both RGB and depth information. Our fully annotated dataset includes seven expressions (i.e., neutral, sadness, disgust, fear, happiness, anger, and surprise) for 15 subjects (9 males and 6 females) aged from 20 to 25. The two individual CNNs are identical in architecture but do not share parameters. To combine the detection results produced by these two CNNs, we propose the late fusion approach. The experimental results demonstrate that the proposed two-stream network using RGB-D images is superior to that of using only RGB images or depth images.

Residual Attention Regression for 3D Hand Pose Estimation

A Novel Convolutional Neural Network for Facial Expression Recognition

Identification of ASD Children based on Video Data

Human fall detection using normalized shape aspect ratio

Recognition of pedestrian activity based on dropped-object detection

Hand Gesture Recognition with Generalized Hough Transform and DC-CNN Using Realsense

A Novel Hand Gesture Recognition Based on High-Level Features

An Integrative Framework of Human Hand Gesture Segmentation for Human–Robot Interaction

Robust Visual Tracking Based on Improved Perceptual Hashing for Robot Vision

Visual tracking based on improved foreground detection and perceptual hashing

A new image-based immersive tool for dementia diagnosis using pairwise ranking and learning

Using Relevance Feedback to Distinguish the Changes in EEG During Different Absence Seizure Phases

Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

A human-oriented mutual assistive framework using collaborative filtering towards disasters

A novel marker-less lung tumor localization strategy on low-rank fluoroscopic images with similarity learning

Classifying Normal and Abnormal Status Based on Video Recordings of Epileptic Patients

Based on video recordings of the movement of the patients with epilepsy, this paper proposed a human action recognition scheme to detect distinct motion patterns and to distinguish the normal status from the abnormal status of epileptic patients. The scheme first extracts local features and holistic features, which are complementary to each other. Afterwards, a support vector machine is applied to classification. Based on the experimental results, this scheme obtains a satisfactory classification result and provides a fundamental analysis towards the human-robot interaction with socially assistive robots in caring the patients with epilepsy (or other patients with brain disorders) in order to protect them from injury.

Building recognition in urban environments: A survey of state-of-the-art and future challenges

Nonconvex resource control and lifetime optimization in wireless video sensor networks based on chaotic particle swarm optimization

Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis

Building Recognition Using Local Oriented Features

Permutation Conditional Mutual Information and Its Application to Epileptic EEG

A novel marker-less tumor tracking strategyonlow-rank fluoroscopic images for image-guided lung cancer radiotherapy

Relevance feedback-based building recognition

Subspace learning-based dimensionality reduction in building recognition

KPCA for semantic object extraction in images

A comprehensive review of current local features for computer vision

A SIMILARITY METRIC IN IMAGE SEARCHING

Multitraining Support Vector Machine for Image Retrieval