The evaluation of the gloss of human hair, following wet/dry chemical treatments such as bleaching, dyeing and perming, has received much scientific and commercial attention. Current gloss analysis techniques use constrained viewing conditions where the hair tresses are observed under directional lighting, within a calibrated presentation environment. The hair tresses are classified by applying computational models of the fibres' physical and optical attributes and evaluated by either a panel of human observers, or the computational modelling of gloss intensity distributions processed from captured digital images.The most popular technique used in industry for automatically assessing hair gloss is to digitally capture images of the hair tresses and produce a classification based upon the average gloss intensity distribution. Unfortunately, the results from current computational modelling techniques are often found to be inconsistent when compared to the panel discriminations of human observers.In order to develop a Gloss Evaluation System that produces the same judgements as those produced from both computational models and human psychophysical panel assessments, the human visual system has to be considered. An image based Gloss Evaluation System with gonio-capture capability has been developed, characterised and tested.A new interpretation of the interaction between reflection bands has been identified on the hair tress images and a novel method was developed to segment the diffuse, chroma and specular regions from the image of the hair tress. A new model has been developed, based on Hunter's contrast gloss approach, to quantify the gloss of the human hair tress.Furthermore, a large number of hair tresses have been treated with a range of hair products to simulate different levels of hair shine. The Tresses have been treated with different commercial products. To conduct a psychophysical experiment, one-dimensional scaling paired comparison test, a MATLAB GUI (Graphical user interface) was developed to display images of the hair tress on calibrated screen. Participants were asked to select the image that demonstrated the greatest gloss. To understand what users were attending to and how they used the different reflection bands in their quantification of the gloss of the human hair tress, the GUI was run on an Eye-Tracking System. The results of several gloss evaluation models were compared with the participants' choices from the psychophysical experiment. The novel gloss assessment models developed during this research correlated more closely with the participants' choices and were more sensitive to changes in gloss than the conventional models used in the study.