Approach For Robust Face Recognition Research Articles

(Retracted) Robust face recognition using multimodal data and transfer learning

In recent years, technological advancements in face recognition have sparked numerous research efforts and have opened up a variety of applications in fields such as security, access control, and identity verification. The accuracy of two-dimensional (2D) face recognition is not up to the mark when used in highly illuminated or dark environments. Further, its vulnerability to spoofing makes it a poor choice for security applications. These problems can be easily resolved with the help of three-dimensional (3D) face recognition. However, 3D data comes with its own set of issues and challenges. The resources and computational power required to collect and process 3D data are found to be heavy. Most recent signs of progress in this area have been achieved by training deep neural networks on large datasets, which is computationally costly and time-consuming. To address these issues, instead of using 3D face data directly, we propose the use of a 2.5D representation of 3D face data along with registered 2D face images, which makes it relatively easy to work with in terms of computational power and time requirements. The paper proposes a robust face recognition approach using multi-modal data (2.5 face images along with 2D face images) and transfer learning. The proposed approach is built on ResNet-34 and Siamese network models. The ResNet-34 network is first trained on 2D face images. Further, by reusing the pretrained ResNet-34 network model on 2D images, we perform transfer learning to produce a network that can make accurate predictions on 2.5D images. The final outcome of the face recognition is achieved by fusing the results obtained on 2D and 2.5D data. The proposed approach has been validated on the University of Notre Dame 3D face dataset (ND-Collection D). The experimental analysis shows the effectiveness of the proposed technique.

A singular value decomposition representation based approach for robust face recognition

A singular value decomposition representation based approach for robust face recognition

Fusing magnitude and phase features with multiple face models for robust face recognition

High accuracy face recognition is of great importance for a wide variety of real-world applications. Although significant progress has been made in the last decades, fully automatic face recognition systems have not yet approached the goal of surpassing the human vision system, even in controlled conditions. In this paper, we propose an approach for robust face recognition by fusing two complementary features: one is Gabor magnitude of multiple scales and orientations and the other is Fourier phase encoded by spatial pyramid based local phase quantization (SPLPQ). To reduce the high dimensionality of both features, block-wise fisher discriminant analysis (BFDA) is applied and further combined by score-level fusion. Moreover, inspired by the biological cognitive mechanism, multiple face models are exploited to further boost the robustness of the proposed approach. We evaluate the proposed approach on three challenging databases, i.e., FRGC ver2.0, LFW, and CFW-p, that address two face classification scenarios, i.e., verification and identification. Experimental results consistently exhibit the complementarity of the two features and the performance boost gained by the multiple face models. The proposed approach achieved approximately 96% verification rate when FAR was 0.1% on FRGC ver2.0 Exp.4, impressively surpassing all the best known results.

Robust face recognition with multi-scale Weber local descriptor

This paper presents a simple and novel approach for robust face recognition. Extended multi-scale Weber local descriptor based on unfixed size patches is proposed to obtain micro and macro patterns. Then the recognition is performed using nearest neighborhood method based on Chi-square distance. Voting strategy is utilized in classification to reduce the impacts of the poor quality regions in face image caused by pose, illumination variations. Experiments on CMU-PIE, FERET and AR databases show that the proposed method has higher recognition accuracy in comparison with traditional methods.

A robust face recognition approach through symbolic modeling of Polar FFT features

Face recognition is a challenging and one of the most active area of research in computer vision. Variations in face image due to change in illumination, expression and presence of occlusions along with aging present many challenges to face recognition system. In this paper a new approach to robust face recognition using Polar FFT features modeled as symbolic data is proposed. Initially the face segment is cropped from the image using Viola-Jones algorithm and is converted into gray scale image of size 120 × 120 pixels. 2D-DFT is performed on pre-processed image. The dominant magnitude of 2D-DFT coefficients are computed using polar fourier transform technique and are represented as 1D P-FFT. The magnitude that represents maximum value in 1D P-FFT is considered as a feature value. The extracted feature value is used to construct a symbolic object to represent a face image. Further, a new symbolic similarity measure is devised and employed for assigning test symbolic object to a face class. The performance of the proposed method is evaluated with AR database and an average accuracy of 96.25% is achieved. 100% and 97% verification rate is achieved on the ORL and LFW face databases respectively. The various experiments conducted on AR, ORL and LFW databases using the proposed symbolic data modeling approach shows that this method has a very high degree of recognition and is better compared to some of the recently reported face recognition techniques.

Sparse representation for robust face recognition by dictionary decomposition

Sparse representation-based classification (SRC) method has gained great success in face recognition due to its encouraging and impressive performance. However, in SRC the data used to train or test are usually corrupted, and hence the performance is affected. This paper proposes a robust face recognition approach by means of learning a class-specific dictionary and a projection matrix. Firstly, the training data are decomposed into class-specific dictionary, non-class-specific dictionary, and sparse error matrix. Secondly, in order to correct the corrupted test data, the data are projected onto their corresponding underlying subspace, and a projection matrix between the original training data and the class-specific dictionary is learned. Then, the features of the class-specific dictionary and the corrected test data are extracted by using Eigenface method. Finally, the SRC is performed to classify. Extensive experiments conducted on publicly available data sets show that the proposed algorithm performs better than some state-of-the-art methods.

Extended local binary patterns for face recognition

This paper presents a simple and novel, yet highly effective approach for robust face recognition. Using LBP-like descriptors based on local accumulated pixel differences – Angular Differences and Radial Differences, the local differences were decomposed into complementary components of signs and magnitudes. Based on these descriptors we developed labeled dominant patterns where the most frequently occurring patterns and their labels were learned to capture discriminative textural information. Six histogram features were obtained from each given face image by concatenating spatial histograms extracted from non-overlapping subregions. A whitened PCA technique was used for dimensionality reduction to produce more compact, robust and discriminative features, which were then fused using the nearest neighbor classifier, with Euclidean distance as the similarity measure.We evaluated the effectiveness of the proposed method on the Extended Yale B, the large-scale FERET, and CAS-PEAL-R1 databases, and found that that the proposed method impressively outperforms other well-known systems with a recognition rate of 74.6% on the CAS-PEAL-R1 lighting probe set.

A fast and robust face recognition approach combining Gabor learned dictionaries and collaborative representation

Proposed is a simple yet fast and robust approach to face recognition. This approach is developed specifically to address the challenges due to variations of illumination, expression and occlusion, when studying the facial images of a large population. The proposed approach exploits an improved collaborative representation algorithm. First, we construct an initial dictionary by extracting the multi-scale and multi-orientation Gabor features of the image. Second, we design a new discriminative dictionary by an improved K-SVD algorithm, so that the query sample can be better represented for classification. Finally, l2-norm of coding residual is calculated by collaborative representation based classification with regularized least square method (CRC-RLS) and the class of test sample is obtained. Experiments on two benchmark face datasets show that the proposed method can achieve high classification accuracy and is comparatively low in terms of time-consumption, compared to the CRC-RLS algorithm.