Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

A tied-mixture 2D HMM face recognition system

Abstract: In this paper, a simplified 2D second-order hidden Markov model (HMM) with tied state mixtures is applied to the face recognition problem. The mixture of the model states is fully-tied across all models for lower complexity. Tying HMM parameters is a well-known solution for the problem of insufficient training data leading to nonrobust estimation. We show that parameter tying in HMM also enhances the resolution in the case of small model. The performance of the proposed 2D HMM tied-mixture system is studied for the face recognition problem and the expected improved robustness is confirmed.

Pairwise classifier combination and its application on Chinese character recognition

Abstract: The complexity of pattern recognition increases along with the number of classes to be classified. As a result, large-scale pattern recognition problems such as Chinese character recognition are very difficult. On the other hand, with least number of classes, the research of pairwise classification has well developed theories and applicable methods. Thus, it is very practical to apply pairwise methods to solve multi-class and large-scale pattern recognition problems. In this paper, the multi-class problem has been broken down into a combination of pairwise problems so that multi-class problem can be solved through pairwise classifier combination. The method has been applied in Chinese character recognition. Comparing with primary system, the first candidate recognition rate increases from 89.25% to 97.94%; the error rate is cut by 80.84%.

A new signature approach for retrieval of documents from free-text databases

Abstract: Among the techniques used for retrieval of information from free-text or document databases, signature methods have proven to be more efficient in terms of storage overhead and processing speed. Signature methods, however, present the problem of “false drops” in which a document is identified but does not satisfy the user query. In the signature approaches such as Word Signature, and Superimposed Coding, the number of false drops is directly related to the hashing function selected, signature size, and number of signature buffers used for each document. Hashing functions also generate collisions, which will result in false drops. In addition, these signature methods do not take into account the length of the words or the positional information of the characters that constitute the word. The use of “Don't Care Characters” in the queries, therefore, is not possible. This paper presents a new signature approach in which the sizes of the signature files are dependent on the number of unique symbols in the alphabet, and therefore for all documents containing English text, the size is constant. The signature generated in this technique maintains the positional information of characters and therefore allows for Don't Care Characters to be used in the queries. Implementation results and comparison of this technique to the Superimposed Coding method is presented.

A biometric approach towards recognizing face in various dark illuminations

Abstract: Face Recognition is a computerized biometric modality which automatically identifies an individual's face for the purpose of recognition. The ability to recognize human faces can be categorized under two senses, the former is the biometric identification and the later is the visual perception. The biometric identification can be done by obtaining a person's image and matching the same against the set of known images whereas the later is how the system percepts the familiar faces and recognize them. This paper presents such a biometric identification of the frontal static face image subjected in various dark illuminations. Face Recognition Biometric Systems automatically recognize the individuals based on their physiological characteristics. The research on such areas has been conducted for more than thirty years, but still more processes and better techniques for biometric facial extraction and recognition are required. This paper presents a framework on such issue by integrating the preprocessing method, local feature extractor and a recognizer for face recognition. An automatic FRBS has been developed that uses 1) Local Binary Pattern and 2) k — Nearest Neighbor classifier. Experimental results based on the Yale — B database show that the use of LBP and k-NN is able to improve the face recognition performance in various dark illuminations.

Cancelable Face Recognition Using Random Multiplicative Transform

Abstract: The generation of cancelable and privacy preserving biometric templates is important for the pervasive deployment of biometric technology in a wide variety of applications. This paper presents a novel approach for cancelable biometric authentication using random multiplicative transform. The proposed method transforms the original biometric feature vector through element-wise multiplication with a random vector, and the sorted index numbers of the resulting vector in the transformed domain are stored as the biometric template. The changeability and privacy protecting properties of the generated biometric template are analyzed in detail. The effectiveness of the proposed method is well supported by extensive experimentation on a face verification problem.