Quantifying Gait Similarity: User Authentication and Real-World Challenge

Template-based approaches using acceleration signals have been proposed for gait-based biometric authentication. In daily life a number of real-world factors affect the users’ gait and we investigate their effects on authentication performance. We analyze the effect of walking speed, different shoes, extra load, and the natural variation over days on the gait. Therefore we introduce a statistical Measure of Similarity (MOS) suited for template-based pattern recognition. The MOS and actual authentication show that these factors may affect the gait of an individual at a level comparable to the variations between individuals. A change in walking speed of 1km/h for example has the same MOS of 20% as the in-between individuals’ MOS. This limits the applicability of gait-based authentication approaches. We identify how these real-world factors may be compensated and we discuss the opportunities for gaitbased context-awareness in wearable computing systems.