Dynamic time warping

About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.

Influence of different walking speeds and surfaces on accelerometer-based biometric gait recognition

Abstract: This paper gives an insight about the influence of different walking speeds (slow, normal and fast) and surfaces (flat carpeted, grass, gravel and inclined) on gait recognition. Gait recognition is a type of biometric authentication that operates on behavioral characteristics of human beings. This research utilizes wearable sensors, and we have used a commercially available mobile device. Gait data is collected from 48 subjects for six different walk settings in two sessions on different days to measure same-day and cross-day performance. Gait cycles are extracted and compared using dynamic time warping as distance metric. Different parameter settings are evaluated to optimize the cycle extraction process.

Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack

Abstract: Power side-channel analysis (SCA) has been of immense interest to most embedded designers to evaluate the physical security of the system. This work presents profiling-based cross-device power SCA attacks using deep-learning techniques on 8-bit AVR microcontroller devices running AES-128. First, we show the practical issues that arise in these profiling-based cross-device attacks due to significant device-to-device variations. Second, we show that utilizing principal component analysis (PCA)-based preprocessing and multidevice training, a multilayer perceptron (MLP)-based 256-class classifier can achieve an average accuracy of 99.43% in recovering the first keybyte from all the 30 devices in our data set, even in the presence of significant interdevice variations. Results show that the designed MLP with PCA-based preprocessing outperforms a convolutional neural network (CNN) with four-device training by ~20% in terms of the average test accuracy of cross-device attack for the aligned traces captured using the ChipWhisperer hardware. Finally, to extend the practicality of these cross-device attacks, another preprocessing step, namely, dynamic time warping (DTW) has been utilized to remove any misalignment among the traces, before performing PCA. DTW along with PCA followed by the 256-class MLP classifier provides ≥10.97% higher accuracy than the CNN-based approach for cross-device attack even in the presence of up to 50 time-sample misalignments between the traces.

On-Line Signature Verification by Dynamic Time Warping and Gaussian Mixture Models

Abstract: Handwriting signature is the most diffuse mean for personal identification. Lots of works have been carried out to get reasonable errors rates within automatic signature verification on-line. Most of the algorithms that have been used for matching work by features extraction. This paper deals with the analysis of discriminative powers of the features that can be extracted from an on-line signature, how it's possible to increase those discriminative powers by dynamic time warping as a step in the preprocessing of the signal coming from the tablet. Also it will be covered the influence of this new step in the performance of the Gaussian mixture models algorithm, which has been shown as a successfully algorithm for on-line automatic signature verification in recent studies. A complete experimental evaluation of the algorithm base on dynamic time warping and Gaussian Mixture Models has been conducted on 2500 genuine signatures samples and 2500 skilled forgery samples from 100 users. Those samples are included at the public access MCyT-Signature-Corpus Database.