Soft computing in biometrics : a pragmatic appraisal

The ever increasing spurt in digital crimes such as image manipulation, image tampering, signature forgery, image forgery, illegal transaction, etc. have hard pressed the demand to combat these forms of criminal activities. In this direction, biometrics - the computer-based validation of a persons' identity is becoming more and more essential particularly for high security systems. The essence of biometrics is the measurement of person’s physiological or behavioral characteristics, it enables authentication of a person’s identity. Biometric-based authentication is also becoming increasingly important in computer-based applications because the amount of sensitive data stored in such systems is growing. The new demands of biometric systems are robustness, high recognition rates, capability to handle imprecision, uncertainties of non-statistical kind and magnanimous flexibility. It is exactly here that, the role of soft computing techniques comes to play. The main aim of this write-up is to present a pragmatic view on applications of soft computing techniques in biometrics and to analyze its impact. It is found that soft computing has already made inroads in terms of individual methods or in combination. Applications of varieties of neural networks top the list followed by fuzzy logic and evolutionary algorithms. In a nutshell, the soft computing paradigms are used for biometric tasks such as feature extraction, dimensionality reduction, pattern identification, pattern mapping and the like.

Differences in muscle activity during hand dexterity tasks between women with arthritis and a healthy reference group

Background. Impaired hand function is common in patients with arthritis and it affects performance of daily activities; thus, hand exercises are recommended. There is little information on the extent to which the disease affects activation of the flexor and extensor muscles during these hand-dexterity tasks. The purpose of this study was to compare muscle activation during such tasks in subjects with arthritis and in a healthy reference group. Methods. Muscle activation was measured in m. extensor digitorium communis (EDC) and in m. flexor carpi radialis (FCR) with surface electromyography (EMG) in women with rheumatoid arthritis (RA, n = 20), hand osteoarthritis (HOA, n = 16) and in a healthy reference group (n = 20) during the performance of four daily activity tasks and four hand exercises. Maximal voluntary isometric contraction (MVIC) was measured to enable intermuscular comparisons, and muscle activation is presented as %MVIC. Results. The arthritis group used a higher %MVIC than the reference group in both FCR and EDC when cutting with a pair of scissors, pulling up a zipper and—for the EDC—also when writing with a pen and using a key (p < 0.02). The exercise “rolling dough with flat hands” required the lowest %MVIC and may be less effective in improving muscle strength. Conclusions. Women with arthritis tend to use higher levels of muscle activation in daily tasks than healthy women, and wrist extensors and flexors appear to be equally affected. It is important that hand training programs reflect real-life situations and focus also on extensor strength.