Face verification using error correcting output codes

Kittler, Josef; Ghaderi, Reza; Windeatt, Terry; Matas, Jiřı́

doi:10.1109/cvpr.2001.990552

Cited by 36 publications

(31 citation statements)

References 17 publications

(16 reference statements)

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“…Equidistant codes can be produced by using the BCH method [19], which employs algebraic techniques from Galois field theory. The number of rows is over-produced (BCH requires number to be power of 2), before using properties (11) and (12) to select a subset of strings. Of course these properties provide only necessary conditions for equidistant strings and so cannot be used to generate them in isolation.…”

Section: Summary Of Constraintsmentioning

confidence: 99%

Binary Strings and multiclass learning problems

Windeatt

Ghaderi

2003

Pattern Recognition and String Matching

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Section: Summary Of Constraintsmentioning

confidence: 99%

Binary Strings and multiclass learning problems

Windeatt

Ghaderi

2003

Pattern Recognition and String Matching

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“…First, the original image feature is projected to a proper low-dimensional space with matrix derived from PCA, and then LDA as in (3) is applied to further reduce the feature dimension with the projection matrix . Finally, the transformation matrix is initialized as (7) The relations between , , , and are the same as in (5) and (6).…”

Section: ) Initializationmentioning

confidence: 99%

“…Denote the transformed class centers from the transformation matrix obtained in Section III-B2 as (12) Then, the corresponding objective function is (13) Denoting and , the optimal affine transformation matrix can be obtained by setting the derivative of the objective function to zero (14) Therefore, the optimal is (15) When the matrix is not of full rank, the inverse matrix can be replaced with the pseudo-inverse matrix. Finally, the transformation matrix is reset to be the product of a columnly orthogonal matrix and a diagonal matrix by using singular value decomposition as in (5) and (6).…”

Section: ) Initializationmentioning

confidence: 99%

“…A large family of algorithms have been proposed for the face identification task [1], [7], [15], [16], [18], [20], [21], [24]. Also, many approaches [2], [6], [14] were presented for face verification; however, most of them [4], [9] are directly transplanted from the face identification literature and ignore the intrinsic distinction between these two tasks. Although there were some attempts [8], [17] to distinguish face verification from the identification task, extra postprocessing or online learning is required, which requires an impractical expense in both computation and storage for online learning in real applications.…”

Section: Introductionmentioning

confidence: 99%

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Face Verification With Balanced Thresholds

Yan

Tang

2007

IEEE Trans. on Image Process.

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Abstract-The process of face verification is guided by a prelearned 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 new dimensionality reduction algorithm tailored to the verification task that ensures threshold balance. This is achieved by the following aspects. First, feasibility is guaranteed by employing an affine transformation matrix, instead of the conventional projection matrix, for dimensionality reduction, and, hence, we call the proposed algorithm threshold balanced transformation (TBT). Then, the affine transformation matrix, constrained as the product of an orthogonal matrix and a diagonal matrix, is optimized to improve the threshold balance and classification capability in an iterative manner. Unlike most algorithms for face verification which are directly transplanted from face identification literature, TBT is specifically designed for face verification and clarifies the intrinsic distinction between these two tasks. Experiments on three benchmark face databases demonstrate that TBT significantly outperforms the state-of-the-art subspace techniques for face verification.

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“…To deal with this problem the Error Correcting Output Codes (ECOC) emerged [1]. Due to its ability to correct the bias and variance errors of the base classifiers [5] [11], the ECOC procedure has been applied to a wide range of applications, such as face recognition [16], face verification [10] and handwritten digit recognition [17].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Linear Discriminant Error Correcting Output Codes Using Particle Swarm Optimization

Bouzas

Arvanitopoulos

Tefas

2011

Lecture Notes in Computer Science

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Abstract. Error Correcting Output Codes reveal an efficient strategy in dealing with multi-class classification problems. According to this technique, a multi-class problem is decomposed into several binary ones. On these created sub-problems we apply binary classifiers and then, by combining the acquired solutions, we are able to solve the initial multiclass problem. In this paper we consider the optimization of the Linear Discriminant Error Correcting Output Codes framework using Particle Swarm Optimization. In particular, we apply the Particle Swarm Optimization algorithm in order to optimally select the free parameters that control the split of the initial problem's classes into sub-classes. Moreover, by using the Support Vector Machine as classifier we can additionally apply the Particle Swarm Optimization algorithm to tune its free parameters. Our experimental results show that by applying Particle Swarm Optimization on the Sub-class Linear Discriminant Error Correcting Output Codes framework we get a significant improvement in the classification performance.

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Face verification using error correcting output codes

Abstract: The Error Correcting Output Coding (ECOC)

Cited by 36 publications

References 17 publications

Binary Strings and multiclass learning problems

Binary Strings and multiclass learning problems

Face Verification With Balanced Thresholds

Optimizing Linear Discriminant Error Correcting Output Codes Using Particle Swarm Optimization

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