The identification of individuals using face recognition represents a challengingtask with many applications in everyday life as well as in high security applications.
Since the human face will vary in appearance in the short time as well as in the long time
range, the inherent "slack in operation" of neural networks together with the redundancy
and possibility to generalize are suggestive for implementing such a recognition system.
The problem has important applications to automated security systems, lip
readers, indexing and retrieval of video images, videoconferencing with improved visual
sensation, and artificial intelligence. There are two basic face detection techniques:
content-based methods and color-based methods.
Content-based methods try to identify features in a human face. Most content based
methods were developed for gray scale images to avoid the complexity of
combining the features detected in the
RGB color space. Unfortunately, content-based
techniques are very complex and expensive computationally. Also, if the face is rotated
or partially obscured, the technique has to incorporate other techniques to solve the image
registration and occlusion problems. In addition, it is often difficult to adapt the methods
to color images.
Face detection using artificial neural networks was done by
Rowley. It is robust
but computationally expensive as the whole image has to be scanned at different scales
and orientations. Feature-based (eyes, nose, and mouth) face detection is done by Yow et
al. Statistical model of mutual distance between facial features are used to locate face in
the image. Markov Random Fields have been used to model the spatial distribution of the
grey level intensities of face images. Some of the eye location technique use infrared
lighting to detect eye pupil. Eye location using genetic algorithm has been proposed by
Wechsler . Skin color is used extensively to segment the image, and localize the search
for face. The detection of face using skin color fails when the source of lighting is not
natural. In this paper, motion information is used to reduce the search space for face
detection. It is known that eye regions are usually darker than other facial parts, therefore
probable eye pair regions are extracted by thresholding the image. The eyes pair region
gives the scale and orientation of face, and reduces the search space for face detection
across different scales and orientations. Correlation between averaged face template and
the test pattern is used to verify whether it is a face or not. Recognition of human face is
also challenging in human computer Interaction. The proposed system for face
recognition is based on eigen analysis of edginess representation of face, which is
invariant to illumination to certain extent.
Existing System.
The available or currently using software is unable to find out the bogus
applications. All the information kept in a record or other document. In a passport office,
everyday comes a number of new applications and number of renewals. The applicant has
to fill the form correctly and has to submit to the office staff. The staff will check the
form and enter the information into the system. The number of applicants will be more
and need a database to carry these information.
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