Face Descriptor Research Articles

Multimodal biometrics approach using face and ear recognition to overcome adverse effects of pose changes

A personal identification method is proposed which uses face and ear together to overcome mass information loss resulting from pose changes. Several aspects are mainly considered: First, ears are at both sides of the face. Their physiological position is approximately orthogonal and their information is complementary to each other when the head pose changes. Therefore, fusing the face and ear is reasonable. Second, the texture feature is extracted using a uniform local binary pattern (ULBP) descriptor which is more compact. Third, Haar wavelet transform, blocked-based, and multiscale ideas are taken into account to further strengthen the extracted texture information. Finally, texture features of face and ear are fused using serial strategy, parallel strategy, and kernel canonical correlation analysis to further increase the recognition rate. Experimental results show that it is both fast and robust to use ULBP to extract texture features. Haar wavelet transform, block-based, and multiscale methods can effectively enhance texture information of the face or ear ULBP descriptor. Multimodal biometrics fusion about face and ear is feasible and effective. The recognition rates of the proposed approach outperform remarkably those of the classic principal component analysis (PCA), kernel PCA, or Gabor texture feature extraction method especially when sharp pose change

Local Directional Number Pattern for Face Analysis: Face and Expression Recognition

This paper proposes a novel local feature descriptor, local directional number pattern (LDN), for face analysis, i.e., face and expression recognition. LDN encodes the directional information of the face's textures (i.e., the texture's structure) in a compact way, producing a more discriminative code than current methods. We compute the structure of each micro-pattern with the aid of a compass mask that extracts directional information, and we encode such information using the prominent direction indices (directional numbers) and sign-which allows us to distinguish among similar structural patterns that have different intensity transitions. We divide the face into several regions, and extract the distribution of the LDN features from them. Then, we concatenate these features into a feature vector, and we use it as a face descriptor. We perform several experiments in which our descriptor performs consistently under illumination, noise, expression, and time lapse variations. Moreover, we test our descriptor with different masks to analyze its performance in different face analysis tasks.

Face verification of age separated images under the influence of internal and external factors

In this paper we study the task of face verification of age-separated images with the presence of various internal and external factors. We propose a hierarchical local binary pattern (HLBP) feature descriptor for robust face representation across age. The effective representation by HLBP across minimal age, illumination, and expression variations combined with its hierarchical computation provides a discriminative representation of the face image. The proposed face descriptor is combined with an AdaBoost classification framework to model the face verification task as a two-class problem. Experimental results on the FG-NET and MORPH aging datasets indicate that the performance of the proposed framework is robust with respect to images of both adults and children. A detailed empirical analysis on the effects of internal (age gap, gender, and ethnicity) and external (pose, expressions, facial hair, and glasses) factors in the face verification performance is also studied. The results indicate that the verification accuracy reduces as the age gap between the image pair increases. A quantitative comparison on the effects of gender on verification performance by both humans and the proposed machine learning approach is provided. The analysis indicate that the cues aid humans in verifying image pairs with large age gaps, while it aids machines for all age gaps. However, the cues mislead humans in the case of images of children and extra-personal pairs with large age gaps. Our analyses indicate that the pose and expression variations affect the performance, despite training with such variations, while facial hair and glasses act as discriminative cues. A study on the effects of ethnicity indicate that non-linear algorithms have insignificant effect in performance with the use of both generalized and individual ethnicity models when compared with linear algorithms.

A robust invariant bipolar representation for R 3 surfaces: applied to the face description

In this paper, we intend to introduce a novel invariant curved surface representation under the 3D motion group. It is constructed from the superposition of the two geodesic potentials generated from a given couple of surface points. By sampling this continuous representation, invariant points are extracted from a large neighborhood around these reference points. Different numerical methods are implemented in order to find an efficient approximation in the mean of the shape distance. The inference of small distortions of points positions applied to the reference points is analyzed. We apply the proposed representation to real 3D images. The experimentations are performed on the 3D facial database Bosphorus.

3-D Face Recognition Using eLBP-Based Facial Description and Local Feature Hybrid Matching

This paper presents an effective method for 3-D face recognition using a novel geometric facial representation along with a local feature hybrid matching scheme. The proposed facial surface description is based on a set of facial depth maps extracted by multiscale extended Local Binary Patterns (eLBP) and enables an efficient and accurate description of local shape changes; it thus enhances the distinctiveness of smooth and similar facial range images generated by preprocessing steps. The following matching strategy is SIFT-based and performs in a hybrid way that combines local and holistic analysis, robustly associating the keypoints between two facial representations of the same subject. As a result, the proposed approach proves robust to facial expression variations, partial occlusions, and moderate pose changes, and the last property makes our system registration-free for nearly frontal face models. The proposed method was experimented on three public datasets, i.e. FRGC v2.0, Gavab, and Bosphorus. It displays a rank-one recognition rate of 97.6% and a verification rate of 98.4% at a 0.001 FAR on the FRGC v2.0 database without any face alignment. Additional experiments on the Bosphorus dataset further highlight the advantages of the proposed method with regard to expression changes and external partial occlusions. The last experiment carried out on the Gavab database demonstrates that the entire system can also deal with faces under large pose variations and even partially occluded ones, when only aided by a coarse alignment process.

An ordered topological representation of 3D triangular mesh facial surface: concept and applications

In this article, we present an original unified framework for analyzing, describing, and encoding 3D facial surfaces. This framework allows the derivation of topologically ordered structures from triangular mesh surfaces, addressing thus the lack of ordered structure in such a modality. After describing the foundations of the framework and highlighting its advantages with respect to close representations, we show its adaptability to a variety of facial mesh surface processing tasks which includes mesh regularity assessment, facial surface cropping, facial surface compression, and facial surface alignment. In addition, it can be used for the extraction of a rich variety of local and global face descriptors. We validate this framework by testing it with raw 3D facial mesh surfaces.

Fast multi-scale local phase quantization histogram for face recognition

Multi-scale local phase quantization (MLPQ) is an effective face descriptor for face recognition. In previous work, MLPQ is computed by using Short-term Fourier Transformation (SFT) in local regions and the high-dimension histogram based features are extracted for face representation. This paper tries to improve MLPQ based face recognition in terms of accuracy and efficiency. It has two main contributions. First, a fast MLPQ extraction algorithm is proposed which produces the same results with original MLPQ method but is about three times faster than the original one in practice. Second, a novel feature selection method combining Adaboost and regression is proposed to select the most discriminative and suitable features for the subsequent subspace learning. Experiments on FERET and FRGC ver 2.0 databases validate the effectiveness and efficiency of the proposed method.

A facial sparse descriptor for single image based face recognition

Single image based face recognition under different variations such as occlusion, expression and pose has been recognized as an important task in many real-world applications. The popularly widely used holistic features are easily distorted due to occlusion and some other variations. In order to tackle this problem, the sparse local feature descriptor based recognition methods have become more and more important, and promising performance is obtained. The recently developed SIFT, which detects feature points sparsely and extracts feature locally for object matching between different views and scales, can also benefit single image based face recognition. However, we find in this paper that SIFT should not be directly used for face recognition, because face recognition differs from generic object matching. To this end, we develop a new framework for detecting feature keypoints sparsely, describing feature context and matching feature points between two face images. We call this new proposed framework as Facial Sparse Descriptor (FSD). Experiments are conducted to support our analysis of SIFT, and extensive experiments are also presented to validate the proposed FSD against SIFT and its two variants, two dense local feature descriptor (i.e., LBP and HoG), PCA and Gabor based methods on AR, CMU and FERET databases.

3D human face description: landmarks measures and geometrical features

Distance measures and geometrical features are widely used to describe faces. Generally, they are extracted punctually from landmarks, namely anthropometric reference points. The aims are various, such as face recognition, facial expression recognition, face detection, study of changes in facial morphology due to growth, or dysmorphologies. Most of the time, landmarks were extracted with the help of an algorithm or manually located on the faces. Then, measures are computed or geometrical features are extracted to perform the scope of the study. This paper is intended as a survey collecting and explaining all these features, in order to provide a structured user database of the potential parameters and their characteristics. Firstly, facial soft-tissue landmarks are defined and contextualized; then the various measures are introduced and some results are given; lastly, the most important measures are compared to identify the best one for face recognition applications.

Computing the Principal Local Binary Patterns for face recognition using data mining tools

Local Binary Patterns are considered as one of the texture descriptors with better results; they employ a statistical feature extraction by means of the binarization of the neighborhood of every image pixel with a local threshold determined by the central pixel. The idea of using Local Binary Patterns for face description is motivated by the fact that faces can be seen as a composition of micro-patterns which are properly described by this operator and, consequently, it has become a very popular technique in recent years. In this work, we show a method to calculate the most important or Principal Local Binary Patterns for recognizing faces. To do this, the attribute evaluator algorithm of the data mining tool Weka is used. Furthermore, since we assume that each face region has a different influence on the recognition process, we have designed a 9-region mask and obtained a set of optimized weights for this mask by means of the data mining tool RapidMiner. Our proposal was tested with the FERET database and obtained a recognition rate varying between 90% and 94% when using only 9 uniform Principal Local Binary Patterns, for a database of 843 individuals; thus, we have reduced both the dimension of the feature vectors needed for completing the recognition tasks and the processing time required to compare all the faces in the database.

Interviewing Techniques for Darwinian Facial‐Composite Systems

SummaryEyewitnesses are often asked to describe the appearance of an offender's face, normally as part of a cognitive interview (CI), and then to construct a facial composite of it by selecting hair, eyes, nose, and so on. Recent research indicates that facial composites of this type are rendered much more identifiable when constructors focus on global character (holistic) judgements of the face after having recalled it in detail. Here, we investigated whether components of this so‐called holistic CI (H‐CI) were applicable to newer ‘evolving’ (Darwinian) methods of face construction. We found that the face description component of the interview promoted better‐quality composites than the holistic component, but the most identifiable composites emerged when both components were used together in the same interview as an H‐CI. Composites were also more identifiable following description of all features of the face than an alternative involving description of hair. Implications are discussed for real‐world face construction using evolving systems. Copyright © 2012 John Wiley & Sons, Ltd.

Recognition of blurred faces using local phase pattern

A novel local phase pattern (LPP) descriptor is proposed for blurred face recognition. The descriptor extracts blur invariant features – the local phase pattern information of the Fourier transform computed locally in a window for every image position. In face image analysis, histograms of LPP labels computed within local regions are used as a face descriptor similarly to the widely used local binary pattern (LBP) methodology for face image description. The LPP descriptors are assessed in comparison with LBP and local phase quantisation. The results show that LPP achieves a promising recognition rate, even when the amount of blur significantly increases.

Local Color Vector Binary Patterns From Multichannel Face Images for Face Recognition

This paper proposes a novel face descriptor based on color information, i.e., so-called local color vector binary patterns (LCVBPs), for face recognition (FR). The proposed LCVBP consists of two discriminative patterns: color norm patterns and color angular patterns. In particular, we have designed a method for extracting color angular patterns, which enables to encode the discriminating texture patterns derived from spatial interactions among different spectral-band images. In order to perform FR tasks, the proposed LCVBP feature is generated by combining multiple features extracted from both color norm patterns and color angular patterns. Extensive and comparative experiments have been conducted to evaluate the proposed LCVBP feature on five public databases. Experimental results show that the proposed LCVBP feature is able to yield excellent FR performance for challenging face images. In addition, the effectiveness of the proposed LCVBP feature has successfully been tested by comparing other state-of-the-art face descriptors.

Verbalization effects in facial composite production

Describing a person's face can temporarily interfere with face recognition ability. We explored whether this so-called ‘verbal overshadowing effect’ (VOE) might interfere with the construction of a traditional facial composite, a face produced by the selection of individual features: hair, eyes, mouth, etc. Participants looked at an unfamiliar target and two days later constructed a single composite after (a) describing the face (verbal no-delay), (b) without describing (no-description) or (c) 30 minutes after describing (verbal delay). Composite quality was overall of poor quality but it was worse in the verbal no-delay group relative to the no-description group, suggesting the involvement of a VOE, but equivalent between verbal delay and no-description, suggesting the presence of a ‘release’ from overshadowing. The data support the revised ‘transfer inappropriate retrieval’ explanation of the VOE, and suggest that witnesses to real crimes should not proceed directly from face description to feature selection.

A HYBRID FACE RECOGNITION APPROACH USING LOCAL FUSION OF COMPLEX DUAL-TREE WAVELET COEFFICIENTS AND RIDGELET TRANSFORM

In this paper, we propose novel face recognition method based on local appearance feature extraction using hybrid mode of local ridge-let and fused dual-tree complex wavelet transform (DT-CWT). It provides a local multiscale description of images with good directional selectivity, effective edge representation and invariance to shifts and in-plane rotations. In the dual-tree implementation, two parallel dis-crete wavelet transform (DWT) with different lowpass and highpass filters in different scales are used. The linear combination of sub-bands generated by two parallel DWT is used to generate 6 different directional subbands with complex coefficients. It is insensitive to illumination variations and facial expression changes. 2-D dual-tree complex wavelet transform is less redundant and computationally efficient. The fusion of local DT-CWT coefficients of detail subbands and local Finite Ridgelet Transform (FRIT) coefficients of approxi-mate subbands of DT-CWT are used to extract the facial features which improve the face recognition with small sample size in less computation. The local features based methods have been success-fully applied to face recognition and achieved state-of-the-art per-formance. Normally most of the local appearance based methods the facial features are extracted from several local regions and concate-nated into an enhanced feature vector as a face descriptor. In this approach we divide the face into several (m×m) non-overlapped paral-lelogram blocks instead of square or rectangle blocks. The local mean and standard deviation of hybrid FRIT and fused DT-CWT coeffi-cients are used to describe the face image. Experiments, on two well-known databases, namely, Yale and ORL databases, shows the Local hybrid FRIT and fused DT-CWT approach performs well on illumi-nation, expression and perspective variant faces with single sample compared to PCA and global DT-CWT. Furthermore, in addition to the consistent and promising classification performances, our pro-posed Hybrid Local FRIT and fused DT-CWT based method has a really low computational complexity.

Local features by intensity increasing tendency for face recognition

Local binary patterns (LBP) histogram has gained popularity as face descriptor. However, LBP is limited by its high dimensionality (256). The uniform local binary patterns (ULBP) reduces dimension to 59 at the price of sacrificed performance. In practice, both LBP and ULBP use neighbourhood with no more than eight pixels. This paper proposes a concept named increasing intensity vector (IIV), which specifies local intensity increasing tendency. Based on IIV, two novel local features are proposed: 1) local binary IIV (LBIIV), which extracts IIV from a binarised neighbourhood; 2) local ternary IIV (LTIIV), where the extracted IIV is expressed in ternary mode. Compared to LBP and ULBP, LBIIV has ever fewer patterns (37) without degrading performance, and greatly improves efficiency. Meanwhile, using larger neighbourhood becomes practical. LTIIV then studies an insight into ternary concept of local pattern. Face recognition experiments show that LBIIV and LTIIV outperform LBP and ULBP in accuracy and efficiency.

3D face recognition based on sparse representation

In this paper, we present a novel 3D face recognition algorithm based on the sparse representation. First, a 3D face normalization approach is proposed to deal with the raw faces. Then, three types of facial geometrical features are extracted to describe the 3D faces. Meanwhile, in order to guarantee the feasibility of the sparse representation framework and promote the recognition efficiency, a novel feature ranking scheme based on Fisher linear discriminant analysis (FLDA) is designed to arrange the facial descriptors. Finally, the sparse representation framework is used to collect all the face features, and it addresses the recognition task. The experiments tested on the BJUT-3D and FRGC v2.0 databases demonstrate the validity of the proposed 3D face recognition algorithm, and the necessity of the FLDA ranking scheme in the sparse representation framework.

Alluvial-deltaic model of the middle-upper Jurassic oil-bearing JV1 productive horizon in the east of the Shirotnoe Priobye

Based on core, log data and seismic, alluvial-deltaic model of the JV12 oil-bearing layer from the one of the regions of the Shirotnoe Priobye in Western Siberia was suggested. To establish the vertical and lateral variation in sediments, the section types were revealed based on electrometric facial models and detailed core description. Coherence and acoustic inversion results allowed authors to reveal the areal distribution of lithofacies. Perspective areas for exploration and exploitation drilling were selected.

Crying, Moving, and Keeping It Whole: What Makes Literary Description Vivid?

Vividness is used in a range of senses which often conflate the intensity of an experience with the accuracy of mental images. In this article we consider the vividness of responses to literary descriptions of faces in the light of the psychology of face perception and the neuroscience of the mirror neuron system. We distinguish between intensity of experience and accuracy of mental images and compare two models of reader response to descriptions: the jigsaw model (the reader constructs a mental image from items of verbal information) and the experiential model (the reader has an emotional, embodied, and holistic response). We predict which aspects of facial description will provoke a vivid response in the experiential sense and discuss examples of literary descriptions of faces in the light of these predictions. We conclude with an illustration of how these insights can be used in literary history and interpretation.

Image-Quality-Based Adaptive Face Recognition

The accuracy of automated face recognition systems is greatly affected by intraclass variations between enrollment and identification stages. In particular, changes in lighting conditions is a major contributor to these variations. Common approaches to address the effects of varying lighting conditions include preprocessing face images to normalize intraclass variations and the use of illumination invariant face descriptors. Histogram equalization is a widely used technique in face recognition to normalize variations in illumination. However, normalizing well-lit face images could lead to a decrease in recognition accuracy. The multiresolution property of wavelet transforms is used in face recognition to extract facial feature descriptors at different scales and frequencies. The high-frequency wavelet subbands have shown to provide illumination-invariant face descriptors. However, the approximation wavelet subbands have shown to be a better feature representation for well-lit face images. Fusion of match scores from low- and high-frequency-based face representations have shown to improve recognition accuracy under varying lighting conditions. However, the selection of fusion parameters for different lighting conditions remains unsolved. Motivated by these observations, this paper presents adaptive approaches to face recognition to overcome the adverse effects of varying lighting conditions. Image quality, which is measured in terms of luminance distortion in comparison to a known reference image, will be used as the base for adapting the application of global and region illumination normalization procedures. Image quality is also used to adaptively select fusion parameters for wavelet-based multistream face recognition.