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A Novel Eyebrow Segmentation and Eyebrow Shape-Based Identification

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A Novel Eyebrow Segmentation and Eyebrow Shape-Based Identification A Novel Eyebrow Segmentation and Eyebrow Shape-based Identification T. Hoang Ngan Le, Utsav Prabhu, Marios Savvides Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213, US A {thihoanl,uprabhu,marioss}@andrew.cmu.edu Abstract (a) Recent studies in biometrics have shown that the peri­ ocular region of the face is sufficiently discriminative for robust recognition, and particularly effectivein certain sce­ narios such as extreme occlusions, and illumination vari­ (b) ations where traditional face recognition systems are un­ reliable. In this paper, we first propose a fully automatic, robust and fast graph-cut based eyebrow segmentation tech­ nique to extract the eyebrow shape froma given face image. (e) We then proposean eyebrow shape-based identification sys­ tem for periocular face recognition. Our experiments have been conducted over large datasets fromthe MBGC and AR databases and the resilience of the proposed approach has been evaluated under varying data conditions. The exper­ imental results show that the proposed eyebrow segmenta­ (d) tion achieves high accuracy with an F-Measure of 99.4% and the identification system achieves ratesof 76. 0% on the Figure Some examples of occlusion faces (a), inter-subject AR database and 85.0% on the MBGC database. l. structural dissimilarities between eyebrows (b), intra-subject asymmetry dissimilarities(c), eyebrow shape is quite robust to imaging conditions (d) 1. Introduction The ongoing widespread deployment of biometric sys­ tems for person identification, verification and access con­ metric features (i.e. the eyelids, eyelashes, eyebrows, and trol clearly indicate the impact of these techniques on hu­ the neighboring skin area), have been suggested as a poten­ man society. These systems primarily use automated meth­ tial source for such robust features, either individually or ods for robust measurement and analysis of physiological in conjunction with face recognition techniques. Eyebrows, and/or behavioral traits to obtain unique and stable features in particular, exhibit a wide variety of shapes, colors, and towards recognition. The most popular and well studied textures, and their appearance is influenced by both genetic methods utilize stable characteristics such as fingerprints, and behavioural characteristics, indicating that they can po­ iris patterns, face images, and voice features. However, tentially be used as a biometric feature. We observe that most of these characteristics are prone to certain impedi­ the periocular region of the face, including the eyebrows, ments, resulting in poor performance under non-ideal con­ is unoccluded under many real-world scenarios where face ditions; for example, it is well known that motion blur, recognition systems fail, such as with headgear, mask oc­ occlusions, pose variations, facial expressions and illumi­ clusions, surveillance videos in crowds, etc. Some exam­ nations all significantly degrade the performance of face ples are shown in Figure lea). Furthermore, eyebrows of recognition engines. Consequently, researchers have been different people are observably dissimilar, particularly in exploring many alternative biometric attributes and modal­ width, length, and boundary shape. Figure l(b) shows an ities which are able to endure such conditions without sig­ example of various eyebrow shapes obtained from different nificant performance degradation. Recently, periocular bio- subjects is our second observation. Eyebrows of an individ- ual are typically intrinsically asymmetric (as the old adage eyebrows have recently attracted the attention of many re­ says: "eyebrows should be sisters, not twins"). Thirdly, the searchers in biometrics. Our literature review shows that type and degree of this asymmetry varies between people, eyebrows have been used by many forensic analysts for hence providing a biometric feature by itself. Examples of years to aid in facial recognition tasks, especially when the this asymmetry are shown in Figure l(c). Finally, while face is partially occluded, or when only the periocular re­ color and texture appearances of eyebrows can be severely gion is clearly visible, such as in crowd images. Sadr et influenced by imaging conditions, we believe that eyebrow al. [18] proved that eyebrows are the most salient and sta­ shapes are typically resilient to these conditions because ble features in a human face and play an important role in they are characterized by salient contour edges which are human identification. They found that the absence of eye­ observable under most such conditions. An example of brows in familiar faces leads to a significant level of con­ an person in different lighting conditions is given in Fig­ fusion in identification/recognition. Furthermore, they also ure l(d) where the eyebrow texture changes between panels showed that the eyebrows can be considered to be as robust while the eyebrow shape remains invariant to the illumina­ a feature as the eyes for face recognition. In [1], Bruce, tion changes. It should be noted that due to lack of perma­ et al. concluded that the eyebrows and skin texture cues nence, the eyebrow shape can be considered to be more of a play an important role in gender discrimination. By com­ soft-biometric feature, particularly since conunon cosmetic bining fingerprints with face recognition to form a multi­ procedures can sometimes alter the shape. In this paper, we modal biometrics system, Rahal, et al. in [17] showed that focus on two main objectives: geometry and shape of the eyebrows (such as length, area, angle of each eyebrow, distance between two eyebrows) are 1. Construction of a robust, fast and fully automatic seg­ useful features for face recognition. Instead of using whole mentation approach to extract the eyebrow from a eyebrow region, Li, et al. [lO], [11] selected and cropped given face image. the pure eyebrows. They extracted textural features from the eyebrow and constructed a recognition system which 2. An examination of the contributions of shape struc­ has been tested on a small database of 32 subjects in and tures of eyebrows on both inter-subject structural dis­ 27 subjects in [11] using a k-means and HMM classifiers similarities and intra-subject asymmetry dissimilarities respectively in the Fourier space. Making use of the eye­ towards face recognition (eyebrow shape-based identi­ brows shape concept, Paleari, et al. [15] used the internal fication system). and external positions of both right and left eyebrows to­ gether with other features such as mouth, nose, eye, fore­ To the best of our knowledge, this is the first instant of head, and lips in order to identify subjects. Recently, Dong, an eyebrow-based recognition system which does not re­ et al. [4] investigated the use of shape-based eyebrows quire any manual intervention. An additional contribution features under non-ideal imaging conditions for biometric is the introduction of the previously unexplored influenceof recognition and gender classification. In their work, vari­ eyebrow asymmetry as a weak biometric feature. ous shape-based features from the eyebrows images are ex­ The rest of this paper is organized as follows: In section tracted and compared by three different classifiers: Mini­ 2, we review some previous efforts carried out on identifi­ mum Distance Classifier(MD), Linear Discriminant Analy­ cation and recognition using eyebrow-based matching and sis Classifier (LDA) and Support Vector Machine Classifier on eyebrow segmentation. Section 3 presents our proposed (SVM). eyebrow segmentation approach together with a Local Eye­ brow Active Shape Model (LE-ASM) with 64 landmarks In order to perform eyebrows shape analysis, the eye­ points on eyebrow region. Section 4 describes the proposed brows must be exactly segmented from a given face image. eyebrow shape-based identification system with two ap­ In [8], Kapoor and Picard used the pupil position which was proaches: inter-subject structural dissimilarities and intra­ tracked by an infrared sensitive camera equipped with in­ subject eyebrow asymmetry dissimilarities. Section 5 de­ frared LEDs to extract the images of eyes and eyebrows. In scribes the datasets used to conduct the experiments along their system, template parameters are recovered by Princi­ with the experimental results. Finally, we present some con­ pal Component Analysis (PCA) analysis on these extracted clusions on this work in Section 5.2.3. images using PCA basis. The PCA basis was constructed during the training phase from some example images. In [2], Chen, et al. first segmented rough eyebrows regions 2. Prior Work using a spatially constrained sub-area K-means clustering A principal challenge in face recognition has been the technique and then extracted the eyebrows by the Snake search for robust and unique facial characteristics which are method. Their work is based on some assumptions of eye able to discriminate between individuals. In addition to an­ corners, the upper eye boundary and the position points of alyzing stable features such as fingerprint, face, and iris, the eyebrows. Recently, Ding, et al. [9] [3] used subclass divisions in order to represent a distinct construction of the same facial component and its context. To divide the train­ ing sample into subclasses, their first algorithm is based on a discriminant analysis formulation whereas the second one is an extension of the AdaBoost approach. However, their Figure 2. LE-ASM with 64 landmarks points on eyebrow region. approaches
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