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1 Unconstrained Face Detection
"... Abstract—Face detection, as the first step in automatic facial analysis, has been well studied over the past two decades. However, challenges still remain for face detection in unconstrained scenarios, such as arbitrary pose variations and occlusions. In this paper, we propose a method to address th ..."
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Abstract—Face detection, as the first step in automatic facial analysis, has been well studied over the past two decades. However, challenges still remain for face detection in unconstrained scenarios, such as arbitrary pose variations and occlusions. In this paper, we propose a method to address these challenges in unconstrained face detection. First, a new type of image feature, called Normalized Pixel Difference (NPD) is proposed. NPD feature is computed as the difference to sum ratio between any two pixel intensity values, inspired by the Weber Fraction in experimental psychology. Besides its computational efficiency, the NPD feature has several desirable properties, such as scale invariance, boundedness, and ability to reconstruct the original image. Second, we develop a method for learning the optimal subset of NPD features and their combinations via regression trees, so that complex face manifolds can be partitioned by the learned rules. This way, only a single cascade classifier is needed to handle unconstrained face detection. The proposed face detector is robust in handling pose, occlusion, illumination, blur and low image resolution. Experimental results on three public face datasets (FDDB, GENKI, and CMU-MIT) show that the proposed method outperforms the state-of-the-art methods reported to date in detecting unconstrained faces with arbitrary pose variations and occlusions in cluttered scenes. Index Terms—Unconstrained face detection, normalized pixel difference, regression tree, AdaBoost, cascade classifier, pose, occlusion, blur 1
View Independent Video-Based Face Recognition Using Posterior Probability in Kernel Fisher Discriminant Space
"... Abstract. This paper presents a view independent video-based face recognition method using posterior probability in Kernel Fisher Discriminant (KFD) space. In practical environment, the view of faces changes dynamically. The robustness to view changes is required for video-based face recognition in ..."
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Abstract. This paper presents a view independent video-based face recognition method using posterior probability in Kernel Fisher Discriminant (KFD) space. In practical environment, the view of faces changes dynamically. The robustness to view changes is required for video-based face recognition in practical environment. Since the view changes induces large non-linear variation, kernel-based methods are appropriate. We use KFD analysis to cope with non-linear variation. To classify image sequence, the posterior probability in KFD space is used. KFD analysis assumes that the distribution of each class in high dimensional space is Gaussian. This makes the computation of posterior probability in KFD space easy. The effectiveness of the proposed method is shown by the comparison with the other feature spaces and classification methods. 1
The Effectiveness of Face Detection Algorithms in Unconstrained Crowd Scenes
"... The 2013 Boston Marathon bombing represents a case where automatic facial biometrics tools could have proven invaluable to law enforcement officials, yet the lack of ro-bustness of current tools in unstructured environments lim-ited their utility. In this work, we focus on complications that confoun ..."
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The 2013 Boston Marathon bombing represents a case where automatic facial biometrics tools could have proven invaluable to law enforcement officials, yet the lack of ro-bustness of current tools in unstructured environments lim-ited their utility. In this work, we focus on complications that confound face detection algorithms. We first present a simple multi-pose generalization of the Viola-Jones al-gorithm. Our results on the Face Detection Data set and Benchmark (FDDB) show that it makes a significant im-provement over the state of the art for published algorithms. Conversely, our experiments demonstrate that the improve-ments attained by accommodating multiple poses can be negligible compared to the gains yielded by normalizing scores and using the most appropriate classifier for uncon-trolled data. We conclude with a qualitative evaluation of the proposed algorithm on publicly available images of the Boston Marathon crowds. Although the results of our evalu-ations are encouraging, they confirm that there is still room for improvement in terms of robustness to out-of-plane ro-tation, blur and occlusion. 1.
GALLERY-FREE METHODS FOR DETECTING AND RECOGNIZING PEOPLE AND GROUPS OF INTEREST “IN THE WILD”
, 2014
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 A Fast and Accurate Unconstrained Face Detector
"... Abstract—We propose a method to address challenges in unconstrained face detection, such as arbitrary pose variations and occlusions. First, a new image feature called Normalized Pixel Difference (NPD) is proposed. NPD feature is computed as the difference to sum ratio between two pixel values, insp ..."
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Abstract—We propose a method to address challenges in unconstrained face detection, such as arbitrary pose variations and occlusions. First, a new image feature called Normalized Pixel Difference (NPD) is proposed. NPD feature is computed as the difference to sum ratio between two pixel values, inspired by the Weber Fraction in experimental psychology. The new feature is scale invariant, bounded, and is able to reconstruct the original image. Second, we propose a deep quadratic tree to learn the optimal subset of NPD features and their combinations, so that complex face manifolds can be partitioned by the learned rules. This way, only a single soft-cascade classifier is needed to handle unconstrained face detection. Furthermore, we show that the NPD features can be efficiently obtained from a look up table, and the detection template can be easily scaled, making the proposed face detector very fast. Experimental results on three public face datasets (FDDB, GENKI, and CMU-MIT) show that the proposed method achieves state-of-the-art performance in detecting unconstrained faces with arbitrary pose variations and occlusions in cluttered scenes. Index Terms—Unconstrained face detection, normalized pixel difference, deep quadratic tree, AdaBoost, cascade classifier F 1
