Fast Face Recognition Research Articles

A fast face recognition system based on annealing algorithm to optimize operator parameters

ABSTRACT In order to improve the recognition rate and operational efficiency of the system, a method in which the image features compensation coefficients are optimized by using an improved simulated annealing algorithm is proposed. Firstly, eight computational factors with low computational complexity are given, which can be used to compensate image features. Secondly, the design flow of face recognition algorithm is presented. Thirdly, an improved simulated annealing algorithm is designed to solve the optimal combination of feature compensation coefficients in the face recognition system. Fourthly, the results of the feature compensation coefficients recommended by the improved simulated annealing algorithm are applied to the Efficient Face Recognition Algorithm (EFRA) in this paper, and verified on the simulation platform. Experiments show that the recognition rate can reach 100% when the training images are 6 in ORL. The proposed algorithm also performs well in MU_PIE dataset.

Research on Fast Face Recognition Method Based on Double Decision Subspace

Because a large number of labeled face data samples in special scenes need a large number of training samples with identity markers, and it is impossible to accurately extract the characteristics of small samples, a fast face recognition method based on double decision subspace is proposed. A feature recognition structure based on double decision subspace is constructed to preprocess the face image and separate the local features of several corresponding face images. The local binary pattern is used to extract the local texture features of the face, and the deep convolution network face fast recognition model is constructed. The convolution network is used to share the weight, pool, and downsampling to reduce the complexity of the model. The constructed recognition model is used to recognize the features of the face image, and the fast face recognition is effectively completed. The experimental results show that the designed method has high recognition accuracy, less average recognition time, and good recognition performance.

PIFS Scheme for HEad Pose Estimation Aimed at Faster Face Recognition

In the context of 2D face biometrics, pose represents one challenging intra-class variation that, over the years, has been approached in a number of different ways, such as through inherently pose-invariant features, or by means of specifically trained deep networks, or even by frontalization techniques aimed to restore a canonical pose. To this regard, the idea behind this paper is to perform head pose estimation (HPE) previous to the face recognition step, so that the probe’s resulting yaw, pitch and roll values can be used to possibly find the pose-wise closest elements in the gallery, thus reducing one-to-many matching time. We exploit a common approach to both pose estimation and feature matching, where fractal encoding is used to extract a feature vector and to compare it to a reference template through a metric distance for both HPE and the facial recognition. Experiments conducted on the Biwi Kinect Head Pose database and the AFLW2000 dataset show state-of-the art level of precision for head pose estimation along with a 10x efficiency boost in the overall face recognition task, compared to popular deep learning approaches, whilst achieving also an edge in terms of recognition accuracy.

A fast face recognition based on image gradient compensation for feature description

To improve the efficiency of traditional face recognition techniques, this paper proposes a novel face recognition algorithm called Image Gradient Feature Compensation (IGFC). Based on the gradients along four directions in an image, a fusion algorithm and a compensation method are implemented to obtain features of the original image. In this study, gradient magnitude maps of a face image are calculated along four directions. Fusion gradients and differential fusion gradients are produced by fusing the four gradient magnitude maps of a face image in multiple ways, and they are used as compensation variables to compensate the appropriate coefficients on the original image and build IGFC feature maps of the original face image. Subsequently, IGFC feature maps are divided into several blocks to calculate the concatenated histogram over all blocks, which is in turn utilized as the feature descriptor for face recognition. Principal component analysis (PCA) is used to cut down the number of dimensions in high-dimensional features, which are recognized by the Support Vector Machine (SVM) classifier. Finally, the proposed IGFC method is superior to traditional methods as suggested by verification studies on YALE, ORL, CMU_PIE, and FERET face databases. When the LibSVM parameter was set to ‘-s 0 -t 2 -c 16 -g 0.0009765625’, the algorithm achieved 100% recognition on Yale and ORL data sets, 92.16% on CMU_PIE data sets, and 74.3% on FERET data sets. The average time for simultaneous completion of the data sets examined was 1.93 s, and the algorithm demonstrated a 70.71% higher running efficiency than the CLBP algorithm. Therefore, the proposed algorithm is highly efficient in feature recognition with excellent computational efficiency.

The model of fast face recognition against age interference in deep learning

The model of fast face recognition against age interference in deep learning

The model of fast face recognition against age interference in deep learning

In order to overcome the low recognition efficiency of traditional anti-age face recognition methods, the paper proposes a new anti-age-disturbing face recognition modelling method based on deep learning. Firstly, build a standard face recognition information database and use this as the matching standard for face recognition. Then, construct a deep learning convolutional neural network, install the propagation process and training strategy of deep learning. Lastly, build an age discrimination model, a loss function and an objective function and solve it. On this basis, the facial features are extracted, after matching with the data in the standard database and similarity calculation, the final rapid facial recognition result is obtained. Experimental results show that the highest recognition accuracy of the designed face recognition model is 99.2%, and the recognition speed of the designed model is faster.

3D Face Recognition Based on an Attention Mechanism and Sparse Loss Function

Face recognition is one of the essential applications in computer vision, while current face recognition technology is mainly based on 2D images without depth information, which are easily affected by illumination and facial expressions. This paper presents a fast face recognition algorithm combining 3D point cloud face data with deep learning, focusing on key part of face for recognition with an attention mechanism, and reducing the coding space by the sparse loss function. First, an attention mechanism-based convolutional neural network was constructed to extract facial features to avoid expressions and illumination interference. Second, a Siamese network was trained with a sparse loss function to minimize the face coding space and enhance the separability of the face features. With the FRGC face dataset, the experimental results show that the proposed method could achieve the recognition accuracy of 95.33%.

Learning to Drop Expensive Layers for Fast Face Recognition

Recent years have seen many advances based on Deep Convolutional Neural Networks (DCNNs) in the tasks of face recognition, most of which are developed to pursue high recognition accuracy. In this paper, we propose a novel Fast FAce Recognizer (Fast-FAR), learning to improve the speed of DCNN-based face recognition model without sacrificing recognition accuracy. Our fundamental insight is that the computation increases exponentially with the depth of a network, the easily identifiable face images can be accurately recognized by the cheap features (pixel values at shallow layers), while the challenging samples that exhibit low quality, large pose variations or occlusions need to be processed by the expensive deep layers. The major contribution of this paper is the Reinforcement Learning Agent (RLA), which is proposed to learn a decision policy determined by a reward function. The policy adaptively decides whether the recognition should be performed at an early layer with a high recognition confidence, or proceeding to the subsequent layers, thus significantly reducing feed-forward cost for the easy faces. According to the extensive experiments on the popular face recognition benchmarks, Fast-FAR reduces the inference time by 14.22%, 20.61%, and 7.84% on the dataset LFW, AgeDB-30 and CFP-FP, respectively.

Real-time detection tracking and recognition algorithm based on multi-target faces

At present, face recognition algorithms are facing some problems with poor face tracking and low real-time performance in multi-target recognition scenarios. This paper details a multi-target face real-time detection tracking and recognition algorithm, including three methods of fast-tracking, fast detection, and quick recognition. The first step offers a new network based on GOTURN for achieving fast face tracking. The prior information of the previous frame image used to predict the position of the face boxes at the current frame. The second step is based on MTCNN for face detection, using the prior information of the present structure to avoid generating massive of invalid candidate boxes, thereby achieving rapid detection of faces. Finally, fast face recognition realized by reduced MobileFaceNet. By avoiding repeated exposure and repeated identification of the same target, the algorithm successfully transforms a multi-target scene into a single-target scene. On the OTB2015 and 300_VW test sets, the evaluation trackers tracked faces with an accuracy rate of 92.2% and 99.6% respectively. On the Xiph test set, multi-target detection and tracking face speed reached 102fps on the CPU. Compared with the original MobileFaceNet, the streamlined network has an accuracy rate of 99.1% on LFW, the feature extraction speed increased by 25%, and the model size reduced by 45%. Experimental results show that the algorithm has high recognition accuracy and real-time performance in multi-target recognition scenes.

Face Recognition at a Distance for a Stand-Alone Access Control System.

Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.

一种更快捷的轻量级人脸识别模型

一种更快捷的轻量级人脸识别模型

Intelligent computational techniques for multimodal data

This guest editorial introduces the special issue on “Intelligent Computational Techniques for Multimodal Data”. The goal of this special issue was to solve a variety of real-life problems which have uncertainty, imprecision, vagueness, resulting in high performance applications or prototypes for real time system. The special issue touched different hot topics related to Computer Vision, Computational Biology, Multimedia data mining, High-dimensional multimedia data, Deep convolution network, Deep semantic preserving hashing, High-dimensional multimedia classification, Deep CNN and extended residual units, particle swarm optimization, Cyberbullying detection on social multimedia, Multimedia detection algorithm of malicious nodes, Multimedia based fast face recognition, Diversifying personalized mobile multimedia application, Multimedia image edge extraction algorithm, Rumour veracity detection.

Multimedia based fast face recognition algorithm of speed up robust features

For the problem that the HAAR descriptors in the speed up robust feature (SURF) algorithm cannot make full use of the information around the feature points, the K-Mean clustering technology is used in this paper to improve the SURF, thus proposing a new face recognition algorithm. Firstly, the problem that the main direction is too dependent on the direction of the local area is avoided by expanding the scope of the main direction; then the information around the subblock that is masked by the 3 × 3 window template is made full use of to construct a descriptor with stronger recognition ability; finally, the problems of excessive time consumed and incorrect matching of interest points are solved by introducing the K-Mean clustering idea. The results of the experiment on FERET and Yale face database show that the proposed algorithm has higher recognition rate and efficiency than other face recognition techniques.

Symmetric Uncertainty Based Search Space Reduction for Fast Face Recognition

Face recognition from a large video database involves more search time. This article proposes a symmetric uncertainty based search space reduction (SUSSR) methodology that facilitates faster face recognition in video, making it viable for real time surveillance and authentication applications. The proposed methodology employs symmetric uncertainty based feature subset selection to obtain significant features. Further, Fuzzy C-Means clustering is applied to restrict the search to nearest possible cluster, thus speeding up the recognition process. Kullback Leibler's divergence based similarity measure is employed to recognize the query face in video by matching the query frame with that of stored features in the database. The proposed search space reduction methodology is tested upon benchmark video face datasets namely FJU, YouTube celebrities and synthetic datasets namely MIT-Dataset-I and MIT-Dataset-II. Experimental results demonstrate the effectiveness of the proposed methodology with a 10 increase in recognition accuracy and 35 reduction in recognition time.

Effective and Fast Face Recognition System Using Complementary OC-LBP and HOG Feature Descriptors With SVM Classifier

Selection and implementation of a face descriptor that is both discriminative and computationally efficient is crucial. Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) have been proven effective for face recognition. LBPs are fast to compute and are easy to extract the texture features. OC-LBP descriptors have been proposed to reduce the dimensionality of LBP while increasing the discrimination power. HOG features capture the edge features that are invariant to rotation and light. Owing to the fact that both texture and edge information is important for face representation, this article proposes a framework to combine OC-LBP and HOG. First, OC-LBP and HOG features are extracted, normalized and fused together. Next, classification is achieved using a histogram-based chi-square, square-chord and extended-canberra metrics and SVM with a normalized chi-square kernel. Experiments on three benchmark databases: ORL, Yale and FERET show that the proposed method is fast to compute and outperforms other similar state-of-the-art methods.

Fast face recognition based on fractal theory

Nowadays, people are more and more concerned about accuracy, rapidity and convenience in the process of personal identification. In the field of biology and computer vision, a variety of methods have been proposed, while a proper method for face recognition is still a challenge. Although some reliable systems and advanced methods have been introduced under relatively controlled conditions, their recognition rate or speed is not satisfactory in the general settings. This is especially true when there are variations in pose, illumination, and facial expression. This paper proposed a fast face recognition method based on fractal theory. This method is to compress the facial images to obtain fractal codes and complete face recognition with these codes. Experimental results on Yale, FERET and CMU PIE databases demonstrate the high efficiency of our method in runtime and correct rate.

A Fast Face Recognition Method Based on Fractal Coding

Nowadays, many methods for face recognition are proposed and most of them can obtain good results. However, when these methods are simulated on the platform of the PC, it is hard to apply these methods, especially complex ones to practical devices. This paper uses fractal theory to compress face images and improves the encoding speed with the inherent feature of facial symmetry. To improve the performance of Fractal Neighbor Distance (FND), which is a way of ranging, the degree of similarity between encoded images is defined, and a novel method called Fractal Neighbor Distance-based Classification (FNDC) is presented in this paper. The criterion of FNDC is classifying different samples of the same person as a class. Experimental results on Yale, FERET and CMU PIE databases show the effectiveness of FNDC in face recognition. Then we apply the method to i.MX6 which embeds Android operating system. Actual operating results demonstrated the high efficiency of our method in runtime and correct rate.

Face Recognition using Maximum Variance and SVD of Order Statistics with only Three States of Hidden Markov Model

paper presents a fast face recognition (FR) method using only three states of Hidden Markov Model (HMM), where the number of states is a major effective factor in computational complexity. Most of the researchers believe that each state represents one facial region, so they used five states or more according to the number of facial regions. In this work, a different idea has been proven, where the number of states is independent of the number of facial regions. The image is resized to 56x56, and order-statistic filters are used to improve the preprocessing operations and thereby reducing the influence of the illumination and noise. Up to three coefficients of Singular Value Decomposition (SVD) are utilized to describe overlapped blocks of size 5x56. Experimental results show that the proposed work manages to achieve 100% recognition rate on ORL face database using the maximum variance and two coefficients of SVD and can, therefore, be considered as the fastest face recognition type. Keywordsrecognition, Hidden Markov Model, Order Statistic Filter, Number of states of HMM, Singular Value Decomposition.

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.

HSR: L 1/2-regularized sparse representation for fast face recognition using hierarchical feature selection

In this paper, we propose a novel method for fast face recognition called L 1/2-regularized sparse representation using hierarchical feature selection. By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in. It consists of Gabor wavelets and extreme learning machine auto-encoder (ELM-AE) hierarchically. For Gabor wavelets’ part, local features can be extracted at multiple scales and orientations to form Gabor-feature-based image, which in turn improves the recognition rate. Besides, in the presence of occluded face image, the scale of Gabor-feature-based global dictionary can be compressed accordingly because redundancies exist in Gabor-feature-based occlusion dictionary. For ELM-AE part, the dimension of Gabor-feature-based global dictionary can be compressed because high-dimensional face images can be rapidly represented by low-dimensional feature. By introducing L 1/2 regularization, our approach can produce sparser and more robust representation compared to L 1-regularized sparse representation-based classification (SRC), which also contributes to the decrease of the computational cost in sparse representation. In comparison with related work such as SRC and Gabor-feature-based SRC, experimental results on a variety of face databases demonstrate the great advantage of our method for computational cost. Moreover, we also achieve approximate or even better recognition rate.