An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30° on either side from the front face showed an average face recognition precision of 72.25%-81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.

Facial expression recognition with contextualized histograms

A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.

Palm vein recognition method based on fusion of local Gabor histograms

Gabor features have been shown to be effective for palm vein recognition. This paper presents a novel feature representation method, implementing the fusion of local Gabor histograms （FLGH）, in order to improve the accuracy of palm vein recognition systems. A new local descriptor called local Gabor principal differences patterns （LGPDP） encodes the Gabor magnitude using the local maximum difference （LMD） operator. The corresponding Gabor phase patterns are encoded by local Gabor exclusive OR （XOR） patterns （LGXP）. Fisher＇s linear discriminant （FLD） method is then implemented to reduce the dimensionality of the feature representation. Low-dimensional Gabor magnitude and phase feature vectors are finally fused to enhance accuracy. Experimental results from Institute of Automation, Chinese Academy of sciences （CASIA） database show that the proposed FLGH method achieves better performance by utilizing score-level fusion. The equal error rate （EER） is 0.08%, which outperforms other conventional palm vein recognition methods （EER range from 2.87% to 0.16%）, e.g., the Laplacian palm, minutiae feature, Hessian phase, Eigenvein, local invariant features, mutual foreground local binary patterns （LBP）, and multi-sampling feature fusion methods.

3D Depth Measurement for Holoscopic 3D Imaging System

Holoscopic 3D imaging is a true 3D imaging system mimics fly’s eye technique to acquire a true 3D optical model of a real scene. To reconstruct the 3D image computationally, an efficient implementation of an Auto-Feature-Edge (AFE) descriptor algorithm is required that provides an individual feature detector for integration of 3D information to locate objects in the scene. The AFE descriptor plays a key role in simplifying the detection of both edge-based and region-based objects. The detector is based on a Multi-Quantize Adaptive Local Histogram Analysis (MQALHA) algorithm. This is distinctive for each Feature-Edge (FE) block i.e. the large contrast changes (gradients) in FE are easier to localise. The novelty of this work lies in generating a free-noise 3D-Map (3DM) according to a correlation analysis of region contours. This automatically combines the exploitation of the available depth estimation technique with edge-based feature shape recognition technique. The application area consists of two varied domains, which prove the efficiency and robustness of the approach: a) extracting a set of setting feature-edges, for both tracking and mapping process for 3D depthmap estimation, and b) separation and recognition of focus objects in the scene. Experimental results show that the proposed 3DM technique is performed efficiently compared to the state-of-the-art algorithms.

3D object retrieval based on histogram of local orientation using one-shot score support vector machine

In this paper, a content based descriptor is pro- posed to retrieve 3D models, which employs histogram of local orientation （HLO） as a geometric property of the shape. The proposed 3D model descriptor scheme consists of three steps. In the first step, Poisson equation is utilized to define a 3D model signature. Next, the local orientation is calculated for each voxel of the model using Hessian matrix. As the final step, a histogram-based 3D model descriptor is extracted by accumulating the values of the local orientation in bins. Due to efficiency of Poisson equation in describing the models with various structures, the proposed descriptor is capable of discriminating these models accurately. Since, the inner vox- els have a dominant contribution in the formation of the de- scriptor, sufficient robustness against noise can be achieved. This is because the noise mostly influences the boundary vox- els. Furthermore, we improve the retrieval performance us- ing support vector machine based one-shot score （SVM-OSS） similarity measure, which is more efficient than the conven- tional methods to compute the distance of feature vectors. The rotation normalization is performed employing the prin- cipal component analysis. To demonstrate the applicability of HLO, we implement experimental evaluations of precision- recall curve on ESB, PSB and WM-SHREC databases of 3D models. Experimental results validate the effectiveness of the proposed descriptor compared to some current methods.

Detecting Designated Building Areas From Remote Sensing Images Using Hierarchical Structural Constraints

Automatic detection of a designated building area(DBA)is a research hotspot in the field of target detection using remote sensing images.Target detection is urgently needed for tasks such as illegal building monitoring,dynamic land use monitoring,antiterrorism efforts,and military reconnaissance.The existing detection methods generally have low efficiency and poor detection accuracy due to the large size and complexity of remote sensing scenes.To address the problems of the current detection methods,this paper presents a DBA detection method that uses hierarchical structural constraints in remote sensing images.Our method was conducted in two main stages.(1)During keypoint generation,we proposed a screening method based on structural pattern descriptors.The local pattern feature of the initial keypoints was described by a multilevel local pattern histogram(MLPH)feature;then,we used one-class support vector machine(OC-SVM)merely to screen those building attribute keypoints.(2)To match the screened keypoints,we proposed a reliable DBA detection method based on matching the local structural similarities of the screened keypoints.We achieved precise keypoint matching by calculating the similarities of the local skeletal structures in the neighboring areas around the roughly matched keypoints to achieve DBA detection.We tested the proposed method on building area sets of different types and at different time phases.The experimental results show that the proposed method is both highly accurate and computationally efficient.