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In the present chapter the authors have ventured to explain the process of recognition of physiological and behavioural traits of human-gait and human-face images, where a trait signifies a character on a feature of the human subject. Recognizing physiological and be‐ havioural traits is a knowledge intensive process, which must take into account all variable information of about human gait and human face patterns. Here the trained data consists of a vast corpus of human gait and human face images of subjects of varying ages. Recognition must be done in parallel with both test and trained data sets. The process of recognition of physiological and behavioural traits involves two basic processes: modelling and understand‐ ing. Recognition of human-gait images and human-face images has been done separately. Modelling involves formation of a noise-free artificial human gait model (AHGM) of hu‐ man-gait images and formation of artificial human-face model (AHFM) of human-face im‐ ages. Understanding involves utilization of the hence formed models for recognition of physiological and behavioural traits. Physiological traits of the subject are the measurement of the physical features of the subject for observation of characteristics. The observable char‐ acters may be categorized into four factors: built, height, complexion and hair. Behavioural traits of the subject involve the measurement of the characteristic behaviour of the subject with relevant to four factors: dominance, extroversion, patience and conformity. Recognition in this chapter has been done in two environments: open-air space and clear-under-water space. The current chapter presents a well defined application of high-end computing techniques like soft-computing, utility computing and also some concepts of cloud computing.

Modelling and Simulation for the Recognition of Physiological and Behavioural Traits Through Human Gait and Face Images