Biometrics is in existence from very early times. Fingerprints were used as a non-counterfeited mark since 500 BC. Babylonian merchants used it to straighten out business transactions. The undertakings were recorded in clay tablets that also contained fingerprints. Footprints were also used to distinguish children. Early Egyptians differentiated between traders by their physical attributes. Inked fingerprints of children were taken for identification purpose by Chinese merchants during the 14th century.
Bertillon has been credited with the systematic study of the measurement of human beings. The system developed by him (anthropometry) was used in fighting crime. Francis Galton developed a classification system for fingerprints. By 1936, the concept of using iris pattern for identification was proposed. Later on, the predecessors of modern voice recognition systems were developed. Similarly, iris recognition, signature recognition and hand geometry biometric devices were developed. However, fingerprint recognition ruled the biometric market and would have continued to do so; unless semi-automated face recognition system made its appearance in 1960s.
An attempt was made to automate the semi-automated system in 1970s. Goldstein, Harmon and Lesk used 21 specific markers on the face to automate the recognition. However, the measurements and locations on the face were manually computed. In 1988, Kirby and Sirovich applied algebra techniques to it for accurate results. This was a landmark achievement in Biometric face recognition system. The modern automated facial detection applications were enabled in 1991. The technology came to be used for security purposes.
There are two approaches to this system – Geometric (feature based) and Photometric (view based). Many algorithms were developed in this technology. Three main ones among them are:
* PCA: Principle Components Analysis (PCA) is an approach, where the probe and gallery images must be normalized to line up the eyes and mouth of the subjects within the images.
* LDA: Linear Discriminate Analysis (LDA) is a statistical approach for classifying samples of unknown classes based on training samples with known classes.
* EBGM: Elastic Bunch Graph Matching (EGBM) is an approach, where the non-linear characteristics that are not addressed by the linear analysis methods, are measured for recognition of a face.
Modern biometric face recognition system relies on these algorithms for identification. The computerized technology has made considerable progress in the recent past. It is used in surveillance for security purposes. Wanted criminals, suspected terrorists and missing people can be detected with the help of this scientific know-how. Face recognition is used for public scrutiny in airports, hospitals, schools and other places of crowd gathering.
Biometric face recognition system is a booming technology. The utilization is increasing day-by-day! Civil rights activists are against its widespread use. They believe it is a blow to the privacy of an individual. Private behavior of people may be recorded and abused later. However, its promising role in spotting illegitimate behavior cannot be ignored. The software can be used in combination with Closed-circuit Surveillance Cameras (CCTVs) for smarter surveillance. Solution providers for this system have seen an upsurge. Government, offices and security officials are their main catch.