Diagonal Fisher linear discriminant analysis for efficient face recognition

In this paper, a novel subspace method called diagonal Fisher linear discriminant analysis (DiaFLD) is proposed for face recognition. Unlike conventional principal component analysis and FLD, DiaFLD directly seeks the optimal projection vectors from diagonal face images without image-to-vector transformation. The advantage of the DiaFLD method over the standard 2-dimensional FLD (2DFLD) method is, the former seeks optimal projection vectors by interlacing both row and column information of images while the latter seeks the optimal projection vectors by using only row information of images. Our test results show that the DiaFLD method is superior to standard 2DFLD method and some existing well-known methods

(2D)2LDA: An efficient approach for face recognition

Although 2DLDA algorithm obtains higher recognition accuracy, a vital unresolved problem of 2DLDA is that it needs huge feature matrix for the task of face recognition. To overcome this problem, this paper presents an efficient approach for face image feature extraction, namely, (2D)2LDA method. Experimental results on ORL and Yale database show that the proposed method obtains good recognition accuracy despite having less number of coefficients

Multilingual OCR system for South Indian scripts and English documents: An approach based on Fourier transform and principal component analysis

Character recognition lies at the core of the discipline of pattern recognition where the aim is to represent a sequence of characters taken from an alphabet Kasturi, R., Gorman, L.O., Govindaraju, V., 2002. Document image analysis: a primer. Sadhana 27 (Part 1), 3–22. Though many kinds of features have been developed and their test performances on standard database have been reported, there is still room to improve the recognition rate by developing improved features. In this paper, we present a multilingual character recognition system for printed South Indian scripts (Kannada, Telugu, Tamil and Malayalam) and English documents. South Indian languages are most popular languages in India and around the world. The proposed multilingual character recognition is based on Fourier transform and principal component analysis (PCA), which are two commonly used techniques of image processing and recognition. PCA and Fourier transforms are classical feature extraction and data representation techniques widely used in the area of pattern recognition and computer vision. Our experimental results show the good performance over the data sets considered

Some issues on choices of modalities for multimodal biometric systems

Biometrics-based authentication has advantages over other mechanisms, but there are several variabilities and vulnerabilities that need to be addressed. No single modality or combinations of modalities can be applied universally that is best for all applications. This paper deliberates different combinations of physiological biometric modalities with different levels of fusion. In our experiments, we have selected Face, Palmprint, Finger Knuckle Print, Iris, and Handvein modalities. All themodalities are of image type and publicly available, comprising at least 100 users. Proper selection of modalities for fusion can yield desired level of performance. Through our experiments it is learnt that a multimodal system which is considered just by increasing number of modalities by fusion would not yield the desired level of performance. Many alternate options for increased performance are presented

Multimodal biometric fusion of face and palmprint at various levels

Recent years have witnessed researchers paying enormous attention to design efficient multi-modal biometric systems because of their ability to withstand spoof attacks. Single biometric sometimes fails to extract adequate information for verifying the identity of a person 7. On the other hand, by combining multiple modalities, enhanced performance reliability could be achieved. In this paper, we have fused face and palmprint modalities at all levels of fusion viz sensor level, feature level, decision level and score level. For this purpose, we have selected modality specific feature extraction algorithms for face and palmprint such as LDA and LPQ respectively. Popular databases AR (for face) and PolyU (for Palmprint) were considered for evaluation purposes. Rigorous experiments were conducted both under clean and noisy conditions to ascertain robust level of fusion and impact of fusion strategies at various levels of fusion for these two modalities. Results are substantiated with appropriate analysis