Face recognition for look-alikes: A preliminary study
TL;DR: The analysis shows that neither humans nor automatic face recognition algorithms are efficient in recognizing look-alikes, and an algorithm is proposed to improve the face verification accuracy.
Abstract: One of the major challenges of face recognition is to design a feature extractor and matcher that reduces the intraclass variations and increases the inter-class variations. The feature extraction algorithm has to be robust enough to extract similar features for a particular subject despite variations in quality, pose, illumination, expression, aging, and disguise. The problem is exacerbated when there are two individuals with lower inter-class variations, i.e., look-alikes. In such cases, the intra-class similarity is higher than the inter-class variation for these two individuals. This research explores the problem of look-alike faces and their effect on human performance and automatic face recognition algorithms. There is three fold contribution in this research: firstly, we analyze the human recognition capabilities for look-alike appearances. Secondly, we compare human recognition performance with ten existing face recognition algorithms, and finally, proposed an algorithm to improve the face verification accuracy. The analysis shows that neither humans nor automatic face recognition algorithms are efficient in recognizing look-alikes.
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TL;DR: It is found that Deep Learning (DL) has mostly outperformed numerous methods using manually designed features by automatically learning and extracting important information from facial features, and enable significant visual recognition functions by improving accuracy in most applications.
Abstract: Face is the most considerable constituent that people use to recognize one another. Humans can quickly and easily identify each other by their faces and since facial features are unobtrusive to lighting condition and pose, face remains as a dynamic recognition approach to human. Kinship recognition refers to the task of training a machine to recognize the blood relation between a pair of kin and non-kin faces (verification) based on features extracted from facial images, and to determine the exact type or degree of that relation (identification). Automatic kinship verification and identification is an interesting areas for investigation, and it has a significant impact in many real world applications, for instance, forensic, finding missing family members, and historical and genealogical research. However, kinship recognition is still not largely explored due to insufficient database availability. In this paper we present a survey on issues and challenges in kinship verification and identification, related previous works, current trends and advancements in kinship recognition, and potential applications and research direction for the future. We also found that Deep Learning (DL) has mostly outperformed numerous methods using manually designed features by automatically learning and extracting important information from facial features, and enable significant visual recognition functions by improving accuracy in most applications.
22 citations
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TL;DR: The proposed LGS variants attempt to improve the performance of the face identification system under the influence of pose changes, facial expression changes, illumination variation, makeup, accessories, accessories and facial complexity.
Abstract: This paper reports a face identification system for visible, look–alike and post–surgery face images of individuals using some novel variants which are exploited from local graph structure (LGS). The proposed LGS variants attempt to improve the performance of the face identification system under the influence of pose changes, facial expression changes, illumination variation, makeup, accessories (glasses) and facial complexity (look alike and plastic surgery). The idea is to represent each pixel along with its neighborhood pixels of a face image based on the regenerated directed local graph structure. From the newly defined local graph structure a binary pattern is generated for each pixel and this binary string is then converted into a decimal value and generates a transformed pattern. Finally, this transformed pattern is used to generate a concatenated histogram which is then used for matching and identification by using three well-known classifiers, namely, locally scaled sum of squared differences (LSSD), locally scaled sum of absolute differences (LSAD), and histogram intersection (HI). Unlike prior works, face images do not have to undergo the preprocessing stages as each novel variant deals with local structure of a face image by disregarding other effects. The UMIST, the JAFFE, the Extended Yale Face B, the Look-alike and the Plastic Surgery face databases are used for the evaluation. Extensive experiments on face databases exhibit promising and convincing results. Further, the experimental results have led to a robust identification system which is found to be invariant to different challenges made of due to capturing environment and face modality changes.
20 citations
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TL;DR: This work introduces three real-world open-set face recognition methods across facial plastic surgery changes and a look-alike face by 3D face reconstruction and sparse representation, and proposes likeness dictionary learning.
Abstract: The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face recognition systems in real-world scenarios focus on handling pose, expression and illumination problems on face recognition. In addition to these challenges, when the number of subjects is increased for face recognition, these problems are intensified by look-alike faces for which there are two subjects with lower intra-class variations. In such challenges, the inter-class similarity is higher than the intra-class variation for these two subjects. In fact, these look-alike faces can be created as intrinsic, situation-based and also by facial plastic surgery. This work introduces three real-world open-set face recognition methods across facial plastic surgery changes and a look-alike face by 3D face reconstruction and sparse representation. Since some real-world databases for face recognition do not have multiple images per person in the gallery, with just one image per subject in the gallery, this paper proposes a novel idea to overcome this challenge by 3D modeling from gallery images and synthesizing them for generating several images. Accordingly, a 3D model is initially reconstructed from frontal face images in a real-world gallery. Then, each 3D reconstructed face in the gallery is synthesized to several possible views and a sparse dictionary is generated based on the synthesized face image for each person. Also, a likeness dictionary is defined and its optimization problem is solved by the proposed method. Finally, the face recognition is performed for open-set face recognition using three proposed representation classifications. Promising results are achieved for face recognition across plastic surgery and look-alike faces on three databases including the plastic surgery face, look-alike face and LFW databases compared to several state-of-the-art methods. Also, several real-world and open-set scenarios are performed to evaluate the proposed method on these databases in real-world scenarios. This paper uses 3D reconstructed models to recognize look-alike faces.A feature is extracted from both facial reconstructed depth and texture images.This paper proposes likeness dictionary learning.Three open-set classification methods are proposed for real-world face recognition.
18 citations
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TL;DR: The proposed algorithm generates CAPTCHA that offer better human accuracy and lower attack rates compared to existing approaches.
Abstract: CAPTCHA is one of the Turing tests used to classify human users and automated scripts. Existing CAPTCHAs, especially text-based CAPTCHAs, are used in many applications, however they pose challenges due to language dependency and high attack rates. In this paper, we propose a face recognition-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly find one pair of human face images, that belong to same subject, embedded in a complex background without selecting any nonface image or impostor pair. The proposed algorithm generates CAPTCHA that offer better human accuracy and lower attack rates compared to existing approaches.
17 citations
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27 Jan 2015
TL;DR: The report presents an evolutionary granular approach to address one of the emerging covariates of face recognition, plastic surgery, and a review of different techniques proposed to address theisting covariates, limitations of current techniques and future scope of advancements.
Abstract: Face recognition has found several applications ranging from cross border security, surveillance, access c ontrol, multimedia to forensics. Face recognition under variations due to pose, illumination, and expression has been extensively st udied in literature and several approaches have been proposed to address these covariates. Many applications of face recogn ition require matching face images with variations in age and disg uise such as matching a recent photo with your passport image or image on driver’s license. In literature, techniques have a lso been proposed to recognize face images with variations in age and disguise. These challenges can be grouped as existing covariates of face recognition. However, with ever increasing applica tions of face recognition there has emerged a need to understand ne w fascinating challenges in face recognition, emerging covariates of face recognition. Covariates such as forensic sketches, su rgically altered faces, low resolution faces, and look-alikes or twi ns are some of the challenges that have emerged as new covariates of face recognition. These covariates have important law enfo rcement applications; therefore, it has now become imperativefor current face recognition systems to be robust to these chall enges. This report focuses on three different aspects. First, it pr esents a review of different techniques proposed to address thexisting covariates, limitations of current techniques and future scope of advancements. Second, it presents how themerging covariates have evolved, what are the challenges, proposed techniques , and future research directions for each of these covariates. Fi nally, the report presents an evolutionary granular approach to address one of the emerging covariate, plastic surgery.
15 citations
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References
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TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Abstract: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.
42,225 citations
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TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Abstract: This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper encompasses a detailed description of the detector and descriptor and then explores the effects of the most important parameters. We conclude the article with SURF's application to two challenging, yet converse goals: camera calibration as a special case of image registration, and object recognition. Our experiments underline SURF's usefulness in a broad range of topics in computer vision.
11,276 citations
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TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Abstract: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed
5,237 citations
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