An efficient face verification method in a transformed domain
Introduction
In the last decade significant advances have been achieved on biometrics, especially on face recognition (Jain et al., 1999). This has been possible due to the increase of computational power of the state-of-the-art computers. However, there are several application scenarios where a low-complexity algorithm, which can be implemented on a low-cost processor is desirable. Some examples of this situation are mobile telephone, PDA or standalone control access systems. Probably in these situations the processor will be a fixed point one, and the number of operations per second smaller than the state-of-the-art processors used to develop the best algorithms available nowadays.
Section snippets
Face recognition
Usually, a pattern recognition system consists of two main blocks: feature extraction and classifier. Fig. 1 summarizes this scheme. On the other hand, there are two main approaches for face recognition:
- (a)
Statistical approaches consider the image as a high-dimension vector, where each pixel is mapped to a component of a vector. Due to the high-dimensionality of vectors some vector-dimension reduction algorithm must be used. Typically the Karhunen–Loeve transform (KLT) is applied with a simplified
Walsh–Hadamard transform
The Walsh–Hadamard transform basis functions can be expressed in terms of Hadamard matrices. A Hadamard matrix Hn is a N × N matrix of ±1 values, where N = 2n.
In contrast to error-control coding applications, in signal processing it is better to write the basis functions as rows of the matrix with increasing number of zero crossings.
The ordered Hadamard matrix can be obtained with the following equations (Gonzalez and Woods, 1993):where bk(x) is the kth bit in the
Results
This section evaluates the results achieved using the WHT and compares them with the classical KLT, eigenface, and DCT methods.
Conclusions
We have proposed a new approach to face recognition based on the Walsh–Hadamard transform, which can be easily implemented on a fixed point processor (Faundez-Zanuy et al., 2005). The experimental results reveal that it is competitive with the state-of-the-art statistical approaches to face recognition. Taking advantage of the minor differences of using different transforms (see Jain, 1989, p. 517), emphasis is focused on this items:
- (a)
We check that WHT performs reasonably good using small and
Acknowledgements
This work has been supported by FEDER and MEC, TIC-2003-08382-C05-02, TEC-2006-13141-C03-02.
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