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1,591
Attribute and Simile Classifiers for Face Verification
- In IEEE International Conference on Computer Vision (ICCV
, 2009
"... We present two novel methods for face verification. Our first method – “attribute ” classifiers – uses binary classifiers trained to recognize the presence or absence of describable aspects of visual appearance (e.g., gender, race, and age). Our second method – “simile ” classifiers – removes the ma ..."
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Cited by 325 (14 self)
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We present two novel methods for face verification. Our first method – “attribute ” classifiers – uses binary classifiers trained to recognize the presence or absence of describable aspects of visual appearance (e.g., gender, race, and age). Our second method – “simile ” classifiers – removes
Face verification with balanced thresholds
- IEEE Transactions on Image Processing
, 2007
"... Abstract—The process of face verification is guided by a pre-learned global threshold, which, however, is often inconsistent with class-specific optimal thresholds. It is, hence, beneficial to pursue a balance of the class-specific thresholds in the model-learning stage. In this paper, we present a ..."
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Cited by 1 (0 self)
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Abstract—The process of face verification is guided by a pre-learned global threshold, which, however, is often inconsistent with class-specific optimal thresholds. It is, hence, beneficial to pursue a balance of the class-specific thresholds in the model-learning stage. In this paper, we present a
FACE VERIFICATION FOR ACCESS CONTROL
"... In this chapter, we discuss the research issues and state-of-the-art of face verification for access control. We start by analyzing a typical face verification system for access control, and then explore the dominant technologies in the field. We introduce most of the typical and popular algorithms ..."
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In this chapter, we discuss the research issues and state-of-the-art of face verification for access control. We start by analyzing a typical face verification system for access control, and then explore the dominant technologies in the field. We introduce most of the typical and popular
Combining face verification experts
- IN PROC. OF INT. CONF. ON PATTERN RECOGNITION
, 2002
"... We study the combination of face verification algorithms in order to improve the verification performance. Although the different face experts that we consider provide their output scores on the basis of the same image, they are complementary to each other and their combination, using a simple sum r ..."
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Cited by 13 (2 self)
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We study the combination of face verification algorithms in order to improve the verification performance. Although the different face experts that we consider provide their output scores on the basis of the same image, they are complementary to each other and their combination, using a simple sum
Illumination Invariance for Face Verification
, 2006
"... The task of face verification is made more difficult when the illumination conditions of image capture are not constrained. The differences in illumination conditions between the stored images of the client and the probe image can be lessened by the application of photometric normalisation. Photomet ..."
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Cited by 1 (0 self)
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The task of face verification is made more difficult when the illumination conditions of image capture are not constrained. The differences in illumination conditions between the stored images of the client and the probe image can be lessened by the application of photometric normalisation
Video Based Face Verification
"... This paper introduces a video based face verification system. Stereo d are used to segment facial region from the background. Facial feat d followed by illumination normalization. Faces are represented by nor s. A Support Vector Machine classifier is trained for each person fo e superior performance ..."
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This paper introduces a video based face verification system. Stereo d are used to segment facial region from the background. Facial feat d followed by illumination normalization. Faces are represented by nor s. A Support Vector Machine classifier is trained for each person fo e superior
Face verification via ecoc
- In Proceedings of British Machine Vision Conference 2001
, 2001
"... We develop a novel approach to face verification based on the Error Correcting Output Coding (ECOC) classifier design concept. In the training phase the client set is repeatedly divided into two ECOC specified sub-sets (superclasses) to train a set of binary classifiers. The output of the classifier ..."
Abstract
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Cited by 4 (1 self)
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We develop a novel approach to face verification based on the Error Correcting Output Coding (ECOC) classifier design concept. In the training phase the client set is repeatedly divided into two ECOC specified sub-sets (superclasses) to train a set of binary classifiers. The output
Fusing Face Verification Algorithms
"... Abstract—It has been demonstrated recently that state-ofthe-art face recognition algorithms can surpass human accuracy at matching faces over changes in illumination. The ranking of algorithms and humans by accuracy, however, does not provide information about whether algorithms and humans perform t ..."
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subject-generated similarity scores were added to the PLS analysis. Fusing humans and algorithms increased performance to near-perfect classification accuracy. These results are discussed in terms of maximizing face verification accuracy with hybrid systems consisting of multiple algorithms and humans
A Method Based On Face Verification
"... Abstract. Recent year, the method based on face verification has been one of the most vibrant study aspect in face verification field. The problem of face verification can be defined: imputing the picture or video, identifying or validating by using face database. “video-video ” face verification me ..."
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Abstract. Recent year, the method based on face verification has been one of the most vibrant study aspect in face verification field. The problem of face verification can be defined: imputing the picture or video, identifying or validating by using face database. “video-video ” face verification
ON LOCAL FEATURES FOR FACE VERIFICATION
, 2004
"... We compare four local feature extraction techniques for the task of face verification, namely (ordered in terms of complexity): raw pixels, raw pixels with mean removal, 2D Discrete Cosine Transform (DCT) and local Principal Component Analysis (PCA). The comparison is performed in terms of discrimi ..."
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Cited by 1 (0 self)
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We compare four local feature extraction techniques for the task of face verification, namely (ordered in terms of complexity): raw pixels, raw pixels with mean removal, 2D Discrete Cosine Transform (DCT) and local Principal Component Analysis (PCA). The comparison is performed in terms
Results 1 - 10
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1,591