Facial Feature Analysis for Habit Detection: Lip and Eye Colour in Smoking and Drinking Prediction

Analysing facial features now presents a real possibility for behaviour detection, such as habits prediction, especially on the prediction of activities like smoking and drinking. This review paper explores the utilization of lip and eye colour functionalities as important pointers in the detection of these behaviours through advancements made in image processing and machine learning. We evaluate the effectiveness of a range of computer methods for face cue extraction and interpretation in habitual behaviour prediction by surveying research from the last decade. In the review, issues such as dataset diversity, algorithmic correctness, and ethical constraints are covered while highlighting important correlations between smoking or drinking activities and differences in lip and eye colour. Our results open the door for further study in preventative care and individualized health monitoring by highlighting the potential of face feature analysis as a non-invasive diagnostic tool. This work synthesizes the body of current knowledge while pointing out potential and gaps for additional research in this multidisciplinary topic. In this research, we apply machine learning and image processing methods to explore whether analysis of facial features, specifically lip and eye colour, might be predictive of habitual behaviours such as drinking and smoking. We identify major developments, challenges, and possible directions for further work in this emerging area.