Video-Based Human Respiratory Wavelet Extraction and Identity Recognition

In this paper, we study the problem of human identity recognition using off-angle human faces. Our proposed system is composed of (i) a physiology-based human clustering module and (ii) an identification module based on facial features (nose, mouth, etc.) fetched from face videos. In our proposed methodology we, first, passively extract an important vital sign (breath). Next we cluster human subjects into nostril motion versus nostril non-motion groups, and, then, localize a set of facial features, before we apply feature extraction and matching. Our proposed human identity recognition system is very efficient. It is working well when dealing with breath signals and a combination of different facial components acquired under challenging luminous conditions. This is achieved by using our proposed Motion Classification approach and Feature Clustering technique based on the breathing waveforms we produce. The contributions of this work are three-fold. First, we generated a set of different datasets where we tested our proposed approach. Specifically, we considered six different types of facial components and their combination, to generate face-based video datasets, which present two diverse data collection conditions, i.e., videos acquired in head full frontal pose (baseline) and head looking up pose. Second, we propose an alternative way of passively measuring human breath from face videos. We demonstrate a comparatively identical breath waveform estimation when compared against the breath waveforms produced by an ADInstruments device (baseline) (Adinstruments, http://www.adinstruments.com/ [1]). Third, we demonstrate good human recognition performance based on partial facial features when using the proposed pre-processing Motion Classification and Feature Clustering techniques. Our approach achieves increased identification rates across all datasets used, and it yields a significantly high identification rate, ranging from 96 to 100% when using a single or a combination of facial features. The approach yields an average of 7% rank-1 rate increase, when compared to the baseline scenario. To the best of our knowledge, this is the first time that a biometric recognition system positively exploits human breath waveforms, which when fused with partial facial features, it increases a benchmark face-based recognition performance established using academic face matching algorithms.