Face Image Retrieval Research Articles

An Effective Approach Towards Content-Based Human Facial Image Detection and Retrieval

An Effective Approach Towards Content-Based Human Facial Image Detection and Retrieval

Hybrid Features Extraction for Adaptive Face Images Retrieval

Existing methods of face emotion recognition have been limited in performance in terms of recognition accuracy and execution time. It is highly important to use efficient techniques for improving this performance. In this article, the authors present an automatic facial image retrieval combining the advantages of color normalization by texture estimators with the gradient vector. Starting from a query face image, an efficient algorithm for human face by hybrid feature extraction provides very interesting results.

An Integrated Methods for Refined Facial-Image Retrieval using Sift and Click-Through Data

In the modern era, the emergence of social networking sites paved the way to the people to upload lot of images online. Social sites like Instagram and Flickr allow users to add semantic information to the images in the form of tags. Often these tags are the firsthand semantic data for retrieving the images from the Internet. When a user searches for images in the web, the images with tags relevant to user query are retrieved. Most of the time, these semantic data are not relevant to the content of the image and hence the user gets irrelevant images in contrast to their intended search. This is more common in facial image search. In this paper, we propose an integrated approach to refine the relevance of retrieved facial images. Using Scale Invariant Feature Extraction (SIFT) the facial features of semantically most relevant image and click-through data of all the retrieved images are used to rank and present a meaningful search result. Along with facial features and click through information, the co-occurrence of related tags is also considered. Also we propose the construction of inverted index structure to improve the search performance.

Texture features extraction based on GLCM for face retrieval system

Texture features play an important role in most image retrieval techniques to obtain results of high accuracy. In this work, the face image retrieval method considering texture analysis and statistical features has been proposed. Textile features can also be extracted using the GLCM tool. In this research, the GLCM calculation method involves two phases, first: some of the previous image processing techniques work together to get the best results to determine the big object of the face image (center of face image) then, the gray level co-occurrence matrix GLCM is computed for gray face image and then some statistical texture features with second-order are extracted. In the second phase, the facial texture features are retrieved by finding the minimum distance between texture features of an unknown face image with the texture features of face images that are stored in the database system. The experimental results show that the proposed method is capable to achieve high accuracy degree in face image retrieval.

Cascaded asymmetric local pattern: a novel descriptor for unconstrained facial image recognition and retrieval

Feature description is one of the most frequently studied areas in the expert systems and machine learning. Effective encoding of the images is an essential requirement for accurate matching. These encoding schemes play a significant role in recognition and retrieval systems. Facial recognition systems should be effective enough to accurately recognize individuals under intrinsic and extrinsic variations of the system. The templates or descriptors used in these systems encode spatial relationships of the pixels in the local neighbourhood of an image. Features encoded using these hand crafted descriptors should be robust against variations such as; illumination, background, poses, and expressions. In this paper a novel hand crafted cascaded asymmetric local pattern (CALP) is proposed for retrieval and recognition facial image. The proposed descriptor uniquely encodes relationship amongst the neighbouring pixels in horizontal and vertical directions. The proposed encoding scheme has optimum feature length and shows significant improvement in accuracy under environmental and physiological changes in a facial image. State of the art hand crafted descriptors namely; LBP, LDGP, CSLBP, SLBP and CSLTP are compared with the proposed descriptor on most challenging datasets namely; Caltech-face, LFW, and CASIA-face-v5. Result analysis shows that, the proposed descriptor outperforms state of the art under uncontrolled variations in expressions, background, pose and illumination.

Facial image retrieval on semantic features using adaptive mean genetic algorithm

The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.

An effective texture descriptor for retrieval of biomedical and face images based on co-occurrence of similar center-symmetric local binary edges

In this paper, a new texture descriptor is proposed for face and biomedical image retrieval. The proposed texture descriptor extracts the co-occurrence of similar center-symmetric local binary edges which are computed at radius ‘1’ and ‘2’ of the local neighborhoods. In contrast, one recent related work first extracts the local pattern of the original image using center-symmetric local binary pattern (CSLBP) and then applies gray-level co-occurrence matrix (GLCM) with four directions as a result of which four matrices are obtained in each direction. The proposed descriptor with much less dimensions captures the co-occurrence information between the local CSLBP edges very efficiently and shows encouraging discriminativeness over similar techniques. Finally more spatial information is incorporated by forming the feature vector by concatenating the histograms calculated from non-overlapping blocks of the pattern map. The efficacy of the proposed descriptor is tested under face and biomedical image retrieval in terms of precision and recall. The benchmark databases ORL face, OSIRIX-CT, NEMA-CT, and York MRI are used for performance evaluation and comparison of the proposed technique with other similar techniques.

Efficient Image Recognition and Retrieval on IoT-Assisted Energy-Constrained Platforms From Big Data Repositories

The advanced computational capabilities of many resource constrained devices, such as smartphones have enabled various research areas including image retrieval from big data repositories for numerous Internet of Things (IoT) applications. The major challenges for image retrieval using smartphones in an IoT environment are the computational complexity and storage. To deal with big data in IoT environment for image retrieval, this paper proposes a light-weighted deep learning-based system for energy-constrained devices. The system first detects and crops face regions from an image using Viola–Jones algorithm with additional face and nonface classifier to eliminate the miss-detection problem. Second, the system uses convolutional layers of a cost effective pretrained CNN model with defined features to represent faces. Next, features of the big data repository are indexed to achieve a faster matching process for real-time retrieval. Finally, Euclidean distance is used to find similarity between query and repository images. For experimental evaluation, we created a local facial images dataset, including both single and group facial images. This dataset can be used by other researchers as a benchmark for comparison with other real-time facial image retrieval systems. The experimental results show that our proposed system outperforms other state-of-the-art feature extraction methods in terms of efficiency and retrieval for IoT-assisted energy-constrained platforms.

Elliptical local binary co-occurrence pattern for face image retrieval

Face image retrieval is considered as an active research field in computer vision. This paper describes a novel feature descriptor called elliptical local binary co-occurrence pattern (ELBCoP) for face image retrieval. This descriptor employs a combination of sparse elliptical local binary pattern (ELBP) and grey level co-occurrence matrix (GLCM). Unlike previous approaches, the sparse ELBP consider only 4 horizontal and 4 vertical neighbors from the elliptic shaped neighborhood, and uses 4 simple diagonal neighbors. Then the co-occurrence of pixel pairs, in the three local pattern maps obtained using sparse ELBP, are calculated using GLCM in two selected directions. These two directions of GLCM are selected according to the shape of the elliptic neighborhood. Through GLCM, the proposed ELBCoP extracts the frequency details and also the spatial relationship between the local pattern pairs very effectively. It is also shown that a properly selected subset of ELBCoP can still provide a robust and efficient description of face images, with significant reduction in feature vector dimensions. Experimental results on two benchmark face image databases show superior retrieval performance of ELBCoP and also subset of ELBCoP, when compared to various state of the art methods including a few very recent ones.

R-theta local neighborhood pattern for unconstrained facial image recognition and retrieval

In this paper R-Theta Local Neighborhood Pattern (RTLNP) is proposed for facial image retrieval. RTLNP exploits relationships amongst the pixels in local neighborhood of the reference pixel at different angular and radial widths. The proposed encoding scheme divides the local neighborhood into sectors of equal angular width. These sectors are again divided into subsectors of two radial widths. Average grayscales values of these two subsectors are encoded to generate the micropatterns. Performance of the proposed descriptor has been evaluated and results are compared with the state of the art descriptors e.g. LBP, LTP, CSLBP, CSLTP, Sobel-LBP, LTCoP, LMeP, LDP, LTrP, MBLBP, BRINT and SLBP. The most challenging facial constrained and unconstrained databases, namely; AT&T, CARIA-Face-V5-Cropped, LFW, and Color FERET have been used for showing the efficiency of the proposed descriptor. Proposed descriptor is also tested on near infrared (NIR) face databases; CASIA NIR-VIS 2.0 and PolyU-NIRFD to explore its potential with respect to NIR facial images. Better retrieval rates of RTLNP as compared to the existing state of the art descriptors show the effectiveness of the descriptor

Fast BGP Face Retrieval Based on K-means Clustering

The traditional face image retrieval method is to compare the target picture with all the pictures in the database one by one, resulting in great time consumption. This paper used the binary gradient pattern (BGP) as face feature extractor. Firstly, the database was encoded offline. Then the encoded database was offline clustered. Next, we extracted the feature of the target picture by BGP and found the nearest clustering centre from the database encoding. Finally, the target picture code for retrieval was selected in the target class.

3D local ternary co-occurrence patterns for natural, texture, face and bio medical image retrieval

Abstract In this paper, a novel feature called three dimensional local ternary co-occurrence pattern (3D-LTCoP) is proposed for natural, texture, face and biomedical image retrieval. Standard local binary pattern and its variants like local ternary patterns, local derivative patterns, local tetra patterns etc. encode relationship between reference pixel and neighboring pixels in a two dimensional plane of the image. The edge distribution information in these local patterns are extracted using first-order derivatives and are represented in the form of histogram. Proposed technique of feature representation draws a three dimensional cubical image block in the local region using Gaussian filtered images and extracts relationship between reference pixel and neighboring pixels in five diverse directions of the 3D block. Further, frequency analysis of ternary patterns is performed by storing mutual local directional information in the co-occurrence matrix. Experiments are conducted on six benchmark databases ranging from natural, texture, face to biomedical categories to observe the robustness of the proposed feature. Results are analyzed and compared with typical state-of-the-art local patterns and superiority of the proposed technique is clearly evident in terms of performance evaluation measures.

Attribute-enhanced metric learning for face retrieval

Metric learning is a significant factor for media retrieval. In this paper, we propose an attribute label enhanced metric learning model to assist face image retrieval. Different from general cross-media retrieval, in the proposed model, the information of attribute labels are embedded in a hypergraph metric learning framework for face image retrieval tasks. The attribute labels serve to build a hypergraph, in which each image is abstracted as a vertex and is contained in several hyperedges. The learned hypergraph combines the attribute label to reform the topology of image similarity relationship. With the mined correlation among multiple facial attributes, the reformed metrics incorporates the semantic information in the general image similarity measure. We apply the metric learning strategy to both similarity face retrieval and interactive face retrieval. The proposed metric learning model effectively narrows down the semantic gap between human and machine face perception. The learned distance metric not only increases the precision of similarity retrieval but also speeds up the convergence distinctively in interactive face retrieval.

Semantic facial scores and compact deep transferred descriptors for scalable face image retrieval

Abstract Face retrieval systems intend to locate index of identical faces to a given query face. The performance of these systems heavily relies on the careful analysis of different facial attributes (gender, race, etc.) since these attributes can tolerate some degrees of geometrical distortion, expressions, and occlusions. However, solely employing facial scores fail to add scalability. Besides, owing to the discriminative power of CNN (convolutional neural network) features, recent works have employed a complete set of deep transferred CNN features with a large dimensionality to obtain enhancement; yet these systems require high computational power and are very resource-demanding. This study aims to exploit the distinctive capability of semantic facial attributes while their retrieval results are refined by a proposed subset feature selection to reduce the dimensionality of the deep transferred descriptors. The constructed compact deeply transferred descriptors (CDTD) not only have a largely reduced dimension but also more discriminative power. Lastly, we have proposed a new performance metric which is tailored for the case of face retrieval called ImAP (Individual Mean Average Precision) and is used to evaluate retrieval results. Multiple experiments on two scalable face datasets demonstrated the superior performance of the proposed CDTD model outperforming state-of-the-art face retrieval results.

Discriminative Deep Quantization Hashing for Face Image Retrieval.

This paper proposes a new discriminative deep quantization hashing (DDQH) approach for large-scale face image retrieval by learning discriminative and compact binary codes. It jointly explores the discrete code learning, batch normalization quantization (BNQ) module, and end-to-end learning in one unified framework, which can guarantee the optimal compatibility of hash coding and feature learning. To learn multiscale and robust facial features, a deep network properly stacking several convolution-pooling layers and pooling layers is designed, and the facial features are obtained by fusing the outputs of the last convolutional layer and the last pooling layer. Besides, the prediction errors of the learned binary codes are minimized to learn discriminative binary codes of images. To obtain higher retrieval accuracies, a BNQ module is utilized to control quantization at a moderate level. Experiments are conducted on two widely used data sets, and the proposed DDQH method achieves encouraging improvements over some state-of-the-art hashing approaches.

Feature selection for spatially enhanced LBP: application to face recognition

Block-based local binary patterns a.k.a. enhanced local binary patterns (ELBPs) have proven to be a highly discriminative descriptor for face recognition and image retrieval. Since this descriptor is mainly composed by histograms, little work (if any) has been done for selecting its relevant features (either the bins or the blocks). In this paper, we address feature selection for both the classic ELBP representation and the recently proposed color quaternionic LBP (QLBP). We introduce a filter method for the automatic weighting of attributes or blocks using an improved version of the margin-based iterative search Simba algorithm. This new improved version introduces two main modifications: (i) the hypothesis margin of a given instance is computed by taking into account the K-nearest neighboring examples within the same class as well as the K-nearest neighboring examples with a different label; (ii) the distances between samples and their nearest neighbors are computed using the weighted $$\chi ^2$$ distance instead of the Euclidean one. This algorithm has been compared favorably with several competing feature selection algorithms including the Euclidean-based Simba as well as variance and Fisher score algorithms giving higher performances. The proposed method is useful for other descriptors that are formed by histograms. Experimental results show that the QLBP descriptor allows an improvement of the accuracy in discriminating faces compared with the ELBP. They also show that the obtained selection (attributes or blocks) can either improve recognition performance or maintain it with a significant reduction in the descriptor size.

Co-occurrence of adjacent sparse local ternary patterns: A feature descriptor for texture and face image retrieval

Searching for similar images in large databases is a time-consuming work and an efficient image retrieval system is valuable in this situation. Texture is a prominent feature of image which can extract contents of image, hence a good texture feature extractor is necessary for content based image retrieval process. In this paper, a new texture descriptor is developed which is a combination of Local Ternary Pattern (LTP) and gray level co-occurrence matrix (GLCM). This feature descriptor which is named as CoALTP, inherits the attributes of both LTP and GLCM. First LTPs of pixels are obtained and then using GLCM in four directions, co-relations between pixel pairs are calculated as features. Two texture databases and a face images dataset are used for evaluation of proposed descriptor and results are compared with other local feature descriptors.

Centre symmetric quadruple pattern: A novel descriptor for facial image recognition and retrieval

Facial features are defined as the local relationships that exist amongst the pixels of a facial image. Hand-crafted descriptors identify the relationships of the pixels in the local neighborhood defined by the kernel. Kernel is a two dimensional matrix which is moved across the facial image. Distinctive information captured by the kernel with limited number of pixel achieves satisfactory recognition and retrieval accuracies on facial images taken under constrained environment (controlled variations in light, pose, expressions, and background). To achieve similar accuracies under unconstrained environment local neighborhood has to be increased, in order to encode more pixels. Increasing local neighborhood also increases the feature length of the descriptor. In this paper we propose a hand-crafted descriptor namely Centre Symmetric Quadruple Pattern (CSQP), which is structurally symmetric and encodes the facial asymmetry in quadruple space. The proposed descriptor efficiently encodes larger neighborhood with optimal number of binary bits. It has been shown using average entropy, computed over feature images encoded with the proposed descriptor, that the CSQP captures more meaningful information as compared to state of the art descriptors. The retrieval and recognition accuracies of the proposed descriptor has been compared with state of the art hand-crafted descriptors (CSLBP, CSLTP, LDP, LBP, SLBP and LDGP) on bench mark databases namely; LFW, Color-FERET, and CASIA-face-v5. Result analysis shows that the proposed descriptor performs well under controlled as well as uncontrolled variations in pose, illumination, background and expressions.

Face image retrieval based on shape and texture feature fusion

Humongous amounts of data bring various challenges to face image retrieval. This paper proposes an efficient method to solve those problems. Firstly, we use accurate facial landmark locations as shape features. Secondly, we utilise shape priors to provide discriminative texture features for convolutional neural networks. These shape and texture features are fused to make the learned representation more robust. Finally, in order to increase efficiency, a coarse-tofine search mechanism is exploited to efficiently find similar objects. Extensive experiments on the CASIAWebFace, MSRA-CFW, and LFW datasets illustrate the superiority of our method.

Local quadruple pattern: A novel descriptor for facial image recognition and retrieval

In this paper a novel hand-crafted local quadruple pattern (LQPAT) is proposed for facial image recognition and retrieval. Most of the existing hand-crafted descriptors encode only a limited number of pixels in the local neighborhood. Under unconstrained environment the performance of these descriptors tends to degrade drastically. Major problem in increasing the local neighborhood is that, it also increases the feature length of the descriptor. The proposed descriptor tries to overcome these problems by defining an efficient encoding structure with optimal feature length. The proposed descriptor encodes relations amongst the neighbors in quadruple space. Two micro patterns are computed from the local relationships to form the descriptor. The retrieval and recognition accuracies of the proposed descriptor has been compared with state of the art hand crafted descriptors on bench mark databases namely; Caltech-face, LFW, Color-FERET, and CASIA-face-v5. Result analysis shows that the proposed descriptor performs well under uncontrolled variations.