Guide: Dr. Richa Singh
Team Size: 2
Team Member(s): Prateekshit Pandey
Course: Pattern Recognition
Time Period: Jan'15-May'15
Technologies/Concepts Used: Matlab, Machine Learning, Pattern Recognition
World Drug Report by United Nations Office on Drugs and Crimes in 2014 clearly suggests that during the period 2003-2012 the increase in crime rates for possession for personal use worldwide was due to the increase in the total number of drug users, esp. cannabis and ATS (Amphetamine-Type Stimulants). Also with the recent improvements in the CCTV surveillance and the introduction of wearable video cameras for police officers in the United States and some other countries, a large amount of data is available for biometric analysis. We propose a system which can use the data of face images from such sources and identify faces possibly altered by prolonged narcotic drug usage. Experiments were conducted majorly on before-after drug mug-shot images made public by Multinomah Sheriff County Office. We use three different types of feature extraction techniques: HoG, Local Binary Patterns and Color Histogram, over which we apply a Support Vector Machine with different kernels to classify the face images.