Master of Science in Computer Science
First Committee Member
Evangelos A. Yfantis, Chair
Second Committee Member
Third Committee Member
Laxmi P. Gewali
Graduate Faculty Representative
Number of Pages
The objective of this thesis is to discuss the viability of using pyroelectric infrared (PIR) sensors as a biometric system for human identification.
The human body emits infrared radiation, the distribution of which varies throughout the body, and depends upon the shape and composition of the particular body part. A PIR sensor utilizing a Fresnel lens will respond to this infrared radiation. When a human walks, the motion of the body's individual components form a characteristic gait that is likely to affect a PIR sensor field in a unique way.
A statistical model, such as a Hidden Markov Model, could be used for the identification process. The model would consist of two phases; learning and testing. The learning phase would train the model on a particular feature or signature. The testing phase would take a signature as input and determine which of the trained models it matches with the highest probability.
Biometric identification; Hidden Markov models; Pattern recognition systems; Signal processing--Digital techniques
Computer Engineering | Signal Processing
Suppes, Aron E., "Human identification using pyroelectric infrared sensors" (2009). UNLV Theses, Dissertations, Professional Papers, and Capstones. 975.