
 How to effectively apply AR (Augmented Reality) new technology to create better safety environment for society and enterprises is the concern of many government and enterprise leaders. This research combines AR smart glasses, multi face recognition technology and control system technology to develop a face recognition active security platform which can effectively improve the arrest of criminals and establish a safer social environment.
 


An Active Security System Based on AR Smart Classes and Face Recognition Technology

Face recognition is an important computer vision task affected by several factors. These factors include the face pose of the input image, features used to describe the image, illumination conditions, and facial expression. In this study, a pose-invariant face recognition framework based on large pose detection and facial landmark description is proposed. During the training phase, a large pose detector model is proposed to process the 2D spatial distributions of the detected facial landmarks on a set of face images. This model can detect whether the yaw angle of the face is large or small (semi-frontal face image). This results in two face pose scenarios. Then, a feature descriptor is applied to a set of predefined facial landmarks on a face image for obtaining the feature vectors. These feature vectors are used to train two face recognition models for each person in the database. One for the large pose scenario and the other for the semi-frontal pose scenario. During the testing phase, the large pose detector is used to select a type of face recognition model (large pose or semi-frontal one). The selected model is utilized to determine the identity of the person. In this study, the CMU-PIE database is employed. Three feature descriptors, SIFT, HOG, and LBP, are adopted for comparison. The models used for face recognition are SVM, GMM, and Naive Bayes. The novelty of the proposed method is using a large pose detector to improve the face recognition rate. After performing experimental trials on face images with pose angles ±90°, a performance comparable with state-of-the-art methods is obtained.

Large Pose Detection and Facial Landmark Description for Pose-invariant Face Recognition

Facial landmarks encode critical information about face, which plays an important role in human communications. Accurate detecting and tracking facial landmarks have great potential value in intelligent user interfaces such as human-computer interactions. However, for face images with sever occlusions which may happen in real life such as hand occlusion, gesture occlusion and etc, detecting the facial landmarks is still a challenging problem. In this paper, we present a robust facial landmark detection method for image with occlusions based on Restricted Boltzmann Machine (RBM). We first present a face shape prior model which is constructed based on RBM to model the spatial shape patterns of the face. The detection process is accomplished by combining the prior shape model with the image measurements of facial landmarks. The low accuracy image measurements can be refined by the shape information embedded in the prior model. For the landmarks with severe occlusions, we firstly evaluate and determine the facial landmark occlusions, and replace their image measurements. The new image measurements are then fed into the prior model as evidence to predict the true locations. Evaluation on 3 databases demonstrates that the proposed method can detect facial landmarks accurately under severe occlusion, and achieved significant improvement over the current state of the art methods.

/pdf/facial-landmarks-detection-under-occlusions-via-extended-4ti9ubxmun.pdf

Yongqiang Li

Papers

Facial Landmarks Detection under Occlusions via Extended Restricted Boltzmann Machine