3D Face Recognition Using Anthropometric and Curvelet Features Fusion

Curvelet transform can describe the signal by multiple scales, and multiple directions. In order to improve the performance of 3D face recognition algorithm, we proposed an Anthropometric and Curvelet features fusion-based algorithm for 3D face recognition (Anthropometric Curvelet Fusion Face Recognition, ACFFR). First, the eyes, nose, and mouth feature regions are extracted by the Anthropometric characteristics and curvature features of the human face. Second, Curvelet energy features of the facial feature regions at different scales and different directions are extracted by Curvelet transform. At last, Euclidean distance is used as the similarity between template and objectives. To verify the performance, the proposed algorithm is compared with Anthroface3D and Curveletface3D on the Texas 3D FR database. The experimental results have shown that the proposed algorithm performs well, with equal error rate of 1.75% and accuracy of 97.0%. The algorithm we proposed in this paper has better robustness to expression and light changes than Anthroface3D and Curveletface3D

Gait recognition using GEI and curvelet

Gait energy image (GEI) is composed of static body silhouette and dynamic frequency information of human gait. To achieve fast and efficient gait recognition, combined with the accurate description of the information of details and directions in image by Curvelet transform, a gait recognition method using GEI and Curvelet (GEIC) is presented. Firstly, to gain the gait energy images, the gait cycle is selected according to the aspect ratio. Secondly, Curvelet energy coefficients of the GEI, which are used as gait feature vector, are extracted by Curvelet transform in different scales and different directions. Finally, the gait recognition is accomplished by the K nearest neighbor (KNN) classifier. The experimental results demonstrate that GEIC performs well on CASIA(B) database, with the average accuracy of 86.83%. Compared with GEI+KPCA, GEI+W(2D)2PCA and GEI+(2D) 2 PCA, the algorithm GEIC achieves better robustness in the condition of the person wearing or packaging

3D Face Recognition Using Anthropometric and Curvelet Features Fusion

Curvelet transform can describe the signal by multiple scales, and multiple directions. In order to improve the performance of 3D face recognition algorithm, we proposed an Anthropometric and Curvelet features fusion-based algorithm for 3D face recognition (Anthropometric Curvelet Fusion Face Recognition, ACFFR). First, the eyes, nose, and mouth feature regions are extracted by the Anthropometric characteristics and curvature features of the human face. Second, Curvelet energy features of the facial feature regions at different scales and different directions are extracted by Curvelet transform. At last, Euclidean distance is used as the similarity between template and objectives. To verify the performance, the proposed algorithm is compared with Anthroface3D and Curveletface3D on the Texas 3D FR database. The experimental results have shown that the proposed algorithm performs well, with equal error rate of 1.75% and accuracy of 97.0%. The algorithm we proposed in this paper has better robustness to expression and light changes than Anthroface3D and Curveletface3D

Target Tracking Algorithm Using Angular Point Matching Combined with Compressive Tracking

To solve the problems of tracking errors such as target missing that emerged in compressive tracking (CT) algorithm due to factors such as pose variation, illumination change, and occlusion, a novel tracking algorithm combined angular point matching with compressive tracking (APMCCT) was proposed. A sparse measurement matrix was adopted to extract the Haar-like features. The offset of the predicted target position was integrated into the angular point matching, and the new target position was calculated. Furthermore, the updating mechanism of the template was optimized. Experiments on different video sequences have shown that the proposed APMCCT performs better than CT algorithm in terms of accuracy and robustness and adaptability to pose variation, illumination change, and occlusion

Gait Recognition Using GEI and AFDEI

Gait energy image (GEI) preserves the dynamic and static information of a gait sequence. The common static information includes the appearance and shape of the human body and the dynamic information includes the variation of frequency and phase. However, there is no consideration of the time that normalizes each silhouette within the GEI. As regards this problem, this paper proposed the accumulated frame difference energy image (AFDEI), which can reflect the time characteristics. The fusion of the moment invariants extracted from GEI and AFDEI was selected as the gait feature. Then, gait recognition was accomplished using the nearest neighbor classifier based on the Euclidean distance. Finally, to verify the performance, the proposed algorithm was compared with the GEI + 2D-PCA and SFDEI + HMM on the CASIA-B gait database. The experimental results have shown that the proposed algorithm performs better than GEI + 2D-PCA and SFDEI + HMM and meets the real-time requirements