Gait Recognition using Encodings with Flexible Similarity Metrics

Gait signals detectable by sensors on ubiquitous personal devices such as smartphones can reveal characteristics unique to each individual, and thereby offer a new approach to recognizing users. Conventional pattern matching approaches use inner-product based distance measures which are not robust to common variations in time-series analysis (e.g., shifts and stretching). This is unfortunate given that it is well understood that capturing such variations is paramount for model performance. This work shows how machine learning methods which encode gait signals into a feature space based on a dictionary can use convolution and Dynamic TimeWarping (DTW) similarity measures to improve classification accuracy in a variety of situations common to gait recognition. We also show that data augmentation is crucial in gait recognition, as diverse training data in practical applications is very limited. We validate the effectiveness of these methods empirically, and demonstrate the identification of user gait patterns where shift and stretch variations in measurements are substantial. We present a new gait dataset that contains a complete representation of the variations that can be expected in real-world recognition scenarios. We compare our techniques against the current state of the art gait period detection and normalization schemes on our dataset and show improved classification accuracy under all experimental scenarios.