Learning Sparse Feature for Eyeglasses Problem in Face Recognition

Transcription

1 Learning Sparse Feature for Eyeglasses Problem in Face Recognition Dong Yi and Stan Z. Li* Center for Biometrics Security Research & National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences, Beijing, China Abstract Occlusion of eyeglasses, and strong specular reflections on eyeglasses (especially in near infrared (NIR) images), can deteriorate face recognition performance. In this paper, we present a novel method to overcome these problems. The proposed method applies the sparse representation (SR) technique in a local feature space so as to be more tolerant to mis-alignment and abnormal specular pixel values. The SR face features are further transformed by using discriminant analysis. These lead to a good balance between efficiency and robustness. Extensive experiments on a large NIR face database containing 292 persons with/without eyeglasses show the superiority of the proposed method compared with state-of-the-art methods. I. INTRODUCTION Eyeglasses can deteriorate face recognition performance in at least two ways: The first is when the enrolled faces are without eyeglasses whereas the probe faces wear eyeglasses, or vice versa. The second is due to strong specular highlight on glasses. We call these problems the eyeglasses problem for short. The first problem is related to occlusion. Four kinds of methods exist for dealing with occlusion in face recognition: part based methods [13], sub-sampling subspace [10], [7], synthesis based methods [23], and sparse representation (SR) [22]. Occluded area in face images may be lighten or discarded during face recognition. Occlusions also could be restored by some statistical learning or image processing steps [5], [23], [2], [6], and then conventional methods are used on them. For the convenience and practicality, this paper is mainly focused on SR. As reported in [22], SR needs little priori information about occlusion, such as position, size and magnitude, which can obtains good results when the proportion of occlusion is smaller than 60%. The second problem of strong specular reflections may happen while using active lighting to assist face imaging. That is the situation of NIR face recognition, where active near infrared illuminators are used to overcome environmental illumination changes [11], with a consequence that it can cause inaccurate eye localization, alignment and recognition. [11] has study the influence of strong specular on eye localization, and proposes a simple-to-complex boosting detector to deal with this problem obtaining satisfactory localization precision. But the influence on recognition is less analyzed in that work, in which just a small scale and unrealistic eyeglasses vs. non-eyeglasses experiment is conducted. In [14], the authors separate the diffuse and *Stan Z. Li is the corresponding author. specular reflection components from a single image by using color and polarization information, but it need additional hardware and cannot apply to monochrome NIR image. Compared with the first problem, strong specular reflection is usually of larger size and higher magnitude (e.g., 255), which can be seen as a more challenging case of occlusion. In this paper, we propose a more effective method to handle the eyeglasses problem by taking advantages of SR. Unlike the existing sparse representation that works on image pixels [22], we apply the sparse linear combination and sparse error model on discriminative local features instead. Because of the locality, sparse error model can still hold when it moves from pixels to local features. We can see in Fig. 1, that the error caused by eyeglasses in pixels and features are sparse concurrent. Meanwhile, local features are more robust to mis-alignment and more discriminative than pixels. Furthermore, the dimensionality of feature is usually much smaller than pixels, which can be computed more efficiently. Therefore our method, called local feature sparse representation, taking the advantages of local feature and sparse representation, can handle the eyeglasses problem and obtains good results on a challenging database. We make careful evaluation by considering different situations of eyeglasses vs. eyeglasses, eyeglasses vs. noneyeglasses, and non-eyeglasses vs. non-eyeglasses between intra and inter persons. Extensive experiments show that the proposed method outperforms the state-of-the-art methods in [11] and [22] significantly. In the case of coarse face alignment (according to two eyes), SR on pixels doesn t show its good performance as reported in [22]. Local feature based methods are always better than pixel based methods consistently. The rest of paper is organized as follows. Section II reviews some related works about occlusions in face recognition, eyeglasses removal and sparse representation. Section III discusses the influence of the eyeglasses problem on face recognition and gives some analysis. Section IV describes the proposed method in detail and its advantages compared to ordinary sparse representation. Section V outlines the database layout and experimental results. Section VI summarizes the paper. II. RELATED WORK The eyeglasses problem is closely related to occlusion, whether eyeglasses or specular highlight. Therefore, this

2 Fig. 1. The sparse error caused by eyeglasses in pixel and LBP feature space. (a) A face image with eyeglasses and specular highlight can be combined by its corresponding ideal (without eyeglasses) face image plus sparse error; (b) The proportion of LBP features selected by boosting in each region (left), the position of boosted 3072 LBP features in the face image (middle), and the sparse error in LBP feature space (right). section will review the related work from the perspective of occlusion in face recognition. As noticed in the early stage of face recognition [20], occlusion could degrade recognition performance shapely, and isn t fully solved today. Much effort has been made to achieve occlusion robust face recognition. In 2000, Leonardis et al. [10] showed the impact of occlusion on object recognition and proposed a sub-sampling based PCA to deal with it. Martinez [13] proposed a parted based method to solve occlusion problem in face recognition, in which each face are divided into many regions, analyzed in isolation and fused in a probabilistic way. In the last ten years, many works followed the direction of sub-sampling and part based method, such as [7], [9]. These works are shown to improve recognition performance, but they are not quite practical due to their computational complexity or needing human intervention (e.g., choosing the layout of partition). Image processing and synthesis techniques have also been used to overcome the eyeglasses occlusion problem in face recognition. In [19] a group of face images without eyeglasses was used to construct a PCA subspace. By projecting a new sample to the non-eyeglasses face subspace, eyeglasses in the face image could be removed with good quality. In 2005, Du et al. [5] improved the PCA method by using an iterated PCA, and achieve better results. Wu et al. [23] proposed an automatic system based on Bayesian inference to remove eyeglasses in face images consisting of three parts: eyeglasses detection, eyeglasses localization and eyeglasses removal. In the FERET [18] and a private database, their method obtained nearly perfect results, but they didn t do any experiments about face recognition. In order to prevent artifacts in the synthesized face images, this kinds of methods need precise key points localization, and are easy to fail while existing specular highlight on eyeglasses. Actually, through some experiments, we found face images after eyeglasses removal obviously lose some details in the eye region, and Fig. 2. Eyeglasses and highlight removal in face images. Top row shows face images with eyeglasses and specular highlight of three different persons. Bottom row is their corresponding results after removing eyeglasses and highlight. The eye region of the processed images become smoother than before. usually reduce recognition performance. Fig. 2 shows some eyeglasses and highlight removal results processed by an algorithm of us, which is not a strict implementation of [23] but as same as it in essence. SR [22] is an important and elegant solution for occlusion robust face recognition proposed recently. As stated by the authors, given a face image as probe, it could be sparsely represented by a linear combination of all face images in gallery plus a sparse error (e.g., partial occlusion). Through a L 1 minimization process [4], the sparse coefficient and occlusion error can be solved simultaneously. Owing to its sparse error model, SR can work well under large occlusion in theory and experiments. But as noted in [21], precise alignment is a key point to SR. Mis-alignment only in serval pixels may result in false non-sparse coefficient, and leading to false reject. Although the authors proposed several precise alignment algorithms in [21] and [17], these algorithms don t suit for large scale face recognition, because of their high complexity. Therefore, we consider mis-alignment robustness is a problem to be solved in SR. Obviously, local features, as a statistics in local regions, are more robust to small misalignment than pixels. III. THE EYEGLASSES PROBLEM IN NIR FACE RECOGNITION Eyeglasses are common in our daily life. During the face enrollment and recognition, many people may wear, change or remove their eyeglasses, so the status of eyeglasses in gallery and probe face images will be uncertain. In particular, active NIR lighting is a necessary component in NIR face recognition system. In face image acquisition process, the frontal active NIR lighting can cause unwanted specular highlight on eyeglasses. In the highlight region, the appearance under eyeglasses are definitely lost, as shown in Fig. 4. If we still use ordinary methods, e.g., PCA, LDA [1] and boosted LBP + LDA [11], on these occluded face images, eyeglasses will confuse the similarity metric in learned feature space and decrease the performance of face recognition. Fig. 3 shows a simple example to illustrate the influence of eyeglasses problem, that the difference between intra

3 TABLE I THE INFLUENCE OF EYEGLASSES ON RECOGNITION AND VALIDATION RATE AT FAR=0.01 UNDER VARIOUS SITUATIONS, WHICH IS EVALUATED USING BOOSTED LBP + LDA [11] Situations Recognition Rate Validation FAR= % 99.61% % 99.28% % 87.43% % 70.21% Fig. 3. Analysis the effects of eyeglasses and specular highlight on similarity scores. Top: Face matching without eyeglasses. Bottom: Face matching disturbed by eyeglasses, images in each column belonging to a person. Middle: Matching score matrix. (diagonal) and inter (off-diagonal) class similarity scores of boosted LBP [11] features are reduced by the eyeglasses and specular highlight. According to the distribution of eyeglasses in face images, the difficulties of the eyeglasses problem can be divided into the following situations from easy to hard: 1) All images without eyeglasses 2) All images with eyeglasses 3) All images in gallery without eyeglasses, and all images in probe with eyeglasses, or vice versa 4) Each person either without eyeglasses in gallery, with eyeglasses in probe or with eyeglasses in gallery, without eyeglasses in probe The state-of-the-art method in [11] can achieve nearly perfect recognition rate in the first and second cases. In the third case, intra and inter similarity scores decrease consistently, thus the recognition rate just drop a little. The fourth case is the most challenging situation we meet in practical applications, in which similarity metric is disturbed by the nearly random eyeglasses occlusion. Table I shows the influence of eyeglasses under various situations on a large database including 292 persons (the database will be described in section V). From the table we find the degradation rate of the fourth situation is the biggest one, especially in validation rate. To be more specific, the experiments in section V will focus on the hardest situation. As reviewed in section II, part based methods may be good to resist occlusions. We can divide each face images into several regions, e.g., four regions as shown in Fig. 1. During recognition, the eye region can be discarded or fused with a weight according to the size of occlusion. Eye region is often one of the most important component in face recognition, e.g., nearly making 1/3 contribution in the example shown in Fig. 1, so whether dropping it or choosing its weight is an obscure problem in real-world systems. Another considerable solution is to synthesize the occluded region by inpaiting [2] or learning based methods [23]. Image inpainting is suitable for texture synthesis but easy to fail on face images. Learning based methods can obtain good results in the human visual sense, as shown in Fig. 2, but don t take any additional information and cause loss of details in the eye region, that will reduce face recognition rate. Therefore, in NIR face recognition, eyeglasses is a very challenging problem must be conquered. Through rough observation, we find that eyeglasses and highlight occlusion in all face images usually don t exceed 40% of whole face. As reported in [22] and [24], under this level of occlusion, SR can work very well. However, a related work [21] and our experiments show SR on pixels is sensitive to mis-alignment. On a coarse aligned database, e.g., alignment according to the coordinates of two eyes, SR on pixels cannot work perfectly as in [22]. In this paper, we propose a local feature SR to obtain a good balance between efficiency and robustness. Fig. 1 shows a eyeglasses occluded NIR face image, a good NIR face image and its residual in pixel and LBP histogram bin feature [15], [12]. From the figure, we can see the residual error are both sparse in pixel and feature domains, so local feature SR is a reasonable solution for eyeglasses problem. The principles and advantages of local feature SR will be discussed in the following section. IV. LOCAL FEATURE SPARSE REPRESENTATION As stated above, local features are more robust to misalignment, more computation efficient than pixels, and sparse error model can hold from pixels to local features. Thus, we combine local feature and SR to solve the eyeglasses problem. In this section, we present two local feature SR methods, one based on multi-scale LBP histogram bins [15], [12], one based on LBP and LDA. As discussed in [11], LBP operator is invariant to monotonically transforming illumination, which achieves very high performance in NIR face recognition, so we choose LBP as basic local feature here. LBP can be applied in various scales and sub-windows. For a face image, huge LBP histogram features are generated, with millions of dimensions. Such high dimensional features are infeasible for practical application and most of them are useless for face recognition actually. Therefore, a small but effective subset is selected from the over-complete feature set by using AdaBoost [8], [11]. All the following steps will be applied on the boosted LBP feature set.

4 A. LBP + Sparse Representation Recently, SR (or compressive sensing) is popular in many fields [3], [4]. One of the most interesting applications is occlusion robust face recognition [22]. Assuming a occlusion in face image is not exceed a ratio (e.g., 60%), we can look the occlusion as a sparse error. A observed face image y can be represented by the following sparse linear combination and error model: y = Ax + e (1) where e is sparse error (occlusion). A is a group of vectorized face images we want to recognize (i.e., gallery). x is sparse linear combination coefficients. In cooperative-user NIR face recognition, face images in probe and gallery are all frontal and illumination uniform, hence any face image y belonging to the ith person will be lie in the linear span of the gallery face images of the ith person. Ideally, x s entries will be zeros except those associated with the ith person, and e will be the occlusion error. Owing to the locality of LBP, sparse error model still can hold, while moving from pixels to local features. We denote the LBP feature of y as y L, the LBP feature of A as A L, so the new model is y L = A L x + e (2) where e is the occlusion in LBP feature space, which is different from the pixel occlusion in Equ. (1). Merge the sparse coefficients and error, Equ. (2) can be written as y L = [ A L, I ][ x e ] = Bz (3) Given y L, A L, solving x and e can be seen as the following L 1 optimization problem min z 1 s.t. y L = Bz The above constraint must be an under-determined system of linear equations. As concluded in [24], many existing algorithms can recover the sparsest solution of problem (4). Considering the overall performance of those algorithms, we use Homotopy [16] in this paper. When the solution is of high-sparsity (e.g., smaller than 40%), Homotopy is very fast and can achieve high performance. As the entries of x encoding the identity information of the probe image y, we use it to do face recognition and validation. Reconstruction residual and sparsity concentration index (SCI) are defined in [22] for face recognition and validation respectively. Without loss of generality, we use a modified SCI (msci) in face recognition and the original SCI for validation experiments. The definition of msci is slightly modified from SCI as follows msci i (x) = n δ i(x) 1 / x 1 1 [0,1] (5) n 1 where i is the index of each class; n is the number of samples in gallery. For each class i, δ i (x) is the characteristic function that selects the coefficients associated with the ith class. The larger the corresponding msci i of a probe y, that y is (4) more similar with the ith class of the gallery. In experiments, (m)sci is used to generate recognition rate (rank1) and ROC curves. Note that, reconstruction residual and msci have very similar recognition performance in face recognition. We use msci here because it s faster than reconstruction residual. B. Extension to LDA LDA is widely used in face recognition for dimension reduction and discriminative feature extraction. Boosted local features are usually of relative higher dimensionality (e.g., ), which would burden the next L 1 minimization step. To improve the performance in accuracy and computation efficiency further, we incorporate LDA into local feature SR. As we know, LDA is a holistic projection operation, it cannot reserve the sparsity of error, that a sparse error (occlusion) in LBP feature space will not remain sparse in the LDA subspace. Despite of this, we still can solve this problem by doing a small modification on Equ. (3). Based on LBP features, we first train a LDA projection matrix P using intra-class and inter-class scatter matrix, then we project the probe and gallery features into the LDA subspace as Py L and PA L. Multiply matrix P on the two sides of Equ. (3), we get Py L = [ PA L, P ][ ] x (6) e Similar to Equ. (4), x and e can be solved by L 1 minimization. Note that e is still a sparse error in LBP feature space not in LDA subspace. (m)sci of x is used for face recognition and validation. V. EXPERIMENTS In this section, we aim to evaluate the performance of the proposed method in NIR face recognition with eyeglasses problem, and compare with conventional local feature method [11] and SR [22]. Five methods are evaluated as follows, 1) Boosted LBP + LDA + nearest neighborhood (NN) [11] 2) Down-sampled image + SR [22] 3) LDA + SR [22] 4) Boosted LBP + SR (proposed) 5) Boosted LBP + LDA + SR (proposed) For equity, all methods are trained and tested on the same databases. To obtain more comprehensive results, all methods are tested in two scenarios: recognition and validation. For a given probe sample, we must first decide if it s a valid sample from one of the persons in gallery (validation), then judge which person the sample belongs to (recognition). These two properties are both crucial for real-world recognition systems. A. Database Setup A database containing NIR face images of 1925 persons are prepared for AdaBoost and LDA training. And we collect an independent database for testing including face

5 Fig. 4. Some aligned face images according to eye coordinates in database images both with/without eyeglasses. The testing database includes 9988 face images of 292 persons, 8-20 images with eyeglasses and 8-20 images without eyeglasses for each person. Some face images in training and testing set are shown in Fig. 4. For the convenience of face recognition and validation, 4 subsets are constructed from the testing database as follows: 1) Gallery for recognition: 292 persons, half of them with eyeglasses, the other half without eyeglasses; 2) Probe for recognition: 292 persons, the other images not overlapped with subset 1); 3) Gallery for validation: randomly selected 146 persons, half of them with eyeglasses, the other half without eyeglasses; 4) Probe for validation: the other images of the same 146 persons in subset 3) as positive samples, all images of the other 146 persons not overlapped with subset 3) as negative samples. The partition rules conforms to the most challenging situation described in section III. Firstly, we detect the eye coordinates of face images by automatic face and eye detectors, and align them to pixels. For every aligned face image, an over-complete LBP features of dimension are extracted in multi-scale way. Then AdaBoost and LDA are both trained on the basis of the LBP features in training set. By using AdaBoost, the high dimensional LBP features can be reduced to 3072 dimensions. LDA further reduces the 3072 LBP features to 256 dimensions. The following experiments are conducted on 3072 or 256 dimensions. B. Face Recognition In this experiment, we test the rank1 recognition rate [18] of the five methods. Table II shows the rank1 recognition rates, where our proposed methods 4) and 5) achieve the best performance: 96.17% and 96.81%. The proposed methods are about 3% higher than conventional NIR face recognition method 1) and 16% higher than SR 3) and 4). Two conventional SR methods are poorer than the other methods, because of their sensitivity to mis-alignment and lack of discriminant. Although method 4) has nearly comparable recognition rate with 5), but is slower than 5). The computational cost per query are also list in table II. All methods are performed in MATLAB 7.8 on 64bits Windows 2003 with dual quad-core 2.66GHz Xeon processors Fig. 5. ROC curves of the five methods for face validation and 16GB of memory. We can see that 1) is the fastest method among them, but it doesn t deal with occlusion explicitly, with lower recognition rate than the proposed methods. Considering the overall (recognition rate and speed) performance, 5) is the most appropriate choice for practical applications, 96.81% recognition rate and 300ms per query against a gallery with 4997 samples. C. Face Validation Next, we demonstrate the performance of our methods for rejecting invalid probe images while accepting valid ones. As described above, the probe set for this experiment are composed by two parts, including 2528 positive samples and 4933 negative samples. By tuning the threshold τ in the range of SCI [0, 1], we generate a series ROC curves for method 2)-5) in Fig. 5. For comparison, the ROC curve of method 1) is generated according to the nearest neighborhood of cosine distance. From Fig. 5 we can see the five methods have similar performance rank with recognition experiment, except for 2) and 3), that is about 5) 4) > 1) > 2) > 3). The best methods 4) and 5) achieve 85.92% validation rate when FAR is 0.01, which has a big improvement (about 13%) compared with the current state-of-the-art method 1). The validation rates are list in table III at FAR=0.1, 0.01 and Considering the overall performance in face face recognition, validation and computational complexity, method 5) LBP+LDA+SR is the best one. VI. CONCLUSIONS Eyeglasses cause problems that must be conquered in face recognition. Although SR provides a good theoretical solution, it is prone to mis-alignment and usually of less discriminant. In this paper, we propose SR in a local feature space and extend it by collaborating with LDA. The experiments show LBP reserves the advantages of SR in dealing with occlusion while becoming less sensitive to mis-alignment

6 TABLE II COMPARISON OF RECOGNITION RATES AND AVERAGE RUN TIME PER QUERY FOR ALL METHODS No Method LBP+LDA+NN Down-sampled+SR LDA+SR LBP+SR LBP+LDA+SR Recognition Rate 93.77% 79.46% 79.82% 96.17% 96.81% Dimension x28= Run Time/Query 0.5ms 460ms 460ms 4100ms 300ms TABLE III COMPARISON OF VALIDATION RATES AT FAR=0.1, 0.01 AND FOR ALL METHODS No Method LBP+LDA+NN Down-sampled+SR LDA+SR LBP+SR LBP+LDA+SR FAR= % 75.83% 76.78% 96.04% 96.52% FAR= % 49.17% 33.98% 85.92% 85.92% FAR= % 32.32% 5.18% 68.83% 62.7% and more discriminant than SR derived from the image pixels. With respect to NIR face recognition with eyeglasses and specular highlight, LBP+SR is obviously better than the conventional NIR face recognition method and original SR in both recognition and validation rate. Moreover, the proposed LDA extension improves the recognition rate and computational speed further. Future work will focus on the choice of other local features and study how to introduce the location priori of eyeglasses into this framework. ACKNOWLEDGEMENTS We thank Shuixian Chen for helpful conversations and comments about this work. This work was supported by the Chinese National Natural Science Foundation Project # , National Science and Technology Support Program Project #2009BAK43B26, and AuthenMetric R&D Funds. REFERENCES [1] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. In Proceedings of the European Conference on Computer Vision, pages 45 58, [2] Marcelo Bertalmio, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester. Image inpainting. In SIGGRAPH 00: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pages , New York, NY, USA, [3] E.J. Candes and T. Tao. Decoding by linear programming. 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