Human Gait Recognition Subject to Different Covariate Factors in a Multi-View Environment

Human gait recognition system identifies individuals based on their biometric traits. A human’s biometric features can be grouped into physiologic or behavioral traits. Biometric traits, such as the face [1], ears [2], iris [3], finger prints, passwords, and tokens, require highly accurate recognition and a well-controlled human interaction to be effective. In contrast, behavioral traits such as voice, signature, and gait do not require any human interaction and can be collected in a hidden and non-invasive mode with a camera system at a low resolution. In comparison with other physiological traits, one of the main advantages of gait analysis is the collection of data from a certain distance. However, gait is less powerful than physiological traits, yet it still has widespread application in surveillance for unfavorable situations. From traditional algorithms to deep learning models, a gait survey provides a detailed history of gait recognition

Detecting Steps Walking at very Low Speeds Combining Outlier Detection, Transition Matrices and Autoencoders from Acceleration Patterns

In this paper, we develop and validate a new algorithm to detect steps while walking at speeds between 30 and 40 steps per minute based on the data sensed from a single tri-axial accelerometer. The algorithm concatenates three consecutive phases. First, an outlier detection is performed on the sensed data based on the Mahalanobis distance to pre-detect candidate points in the acceleration time series that may contain a ground contact segment of data while walking. Second, the acceleration segment around the pre-detected point is used to calculate the transition matrix in order to capture the time dependencies. Finally, autoencoders, trained with data segments containing ground contact transition matrices from acceleration series from labeled steps are used to reconstruct the computed transition matrices at each pre-detected point. A similarity index is used to assess if the pre-selected point contains a true step in the 30-40 steps per minute speed range. Our experimental results, based on a database from three different participants performing similar activities to the target one, are able to achieve a recall = 0.88 with precision = 0.50 improving the results when directly applying the autoencoders to acceleration patterns (recall = 0.77 with precision = 0.50)

Learning a deep-feature clustering model for gait-based individual identification

Gait biometrics which concern with recognizing individuals by the way they walk are of a paramount importance these days. Human gait is a candidate pathway for such identification tasks since other mechanisms can be concealed. Most common methodologies rely on analyzing 2D/3D images captured by surveillance cameras. Thus, the performance of such methods depends heavily on the quality of the images and the appearance variations of individuals. In this study, we describe how gait biometrics could be used in individuals’ identification using a deep feature learning and inertial measurement unit (IMU) technology. We propose a model that recognizes the biological and physical characteristics of individuals, such as gender, age, height, and weight, by examining high-level representations constructed during its learning process. The effectiveness of the proposed model has been demonstrated by a set of experiments with a new gait dataset generated using a shoe-type based on a gait analysis sensor system. The experimental results show that the proposed model can achieve better identification accuracy than existing models, while also demonstrating more stable predictive performance across different classes. This makes the proposed model a promising alternative to current image-based modeling

16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)

The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc

Generative Fingerprint Augmentation against Membership Inference Attacks

openThis thesis aspires to provide a privacy protection mechanism for neural networks concerning fingerprints. Biometric identifiers, especially fingerprints, have become crucial in the last several years, from banking operations to daily smartphone usage. Using generative adversarial networks (GANs), we train models specialized in compressing and decompressing (Codecs) images in order to augment the data these models used during the learning process to provide additional privacy preservation over the identity of the fingerprints found in such datasets. We test and analyze our framework with custom membership inference attacks (MIA) to assess the quality of our defensive mechanism.This thesis aspires to provide a privacy protection mechanism for neural networks concerning fingerprints. Biometric identifiers, especially fingerprints, have become crucial in the last several years, from banking operations to daily smartphone usage. Using generative adversarial networks (GANs), we train models specialized in compressing and decompressing (Codecs) images in order to augment the data these models used during the learning process to provide additional privacy preservation over the identity of the fingerprints found in such datasets. We test and analyze our framework with custom membership inference attacks (MIA) to assess the quality of our defensive mechanism

Deep Learning for Processing Electromyographic Signals: a Taxonomy-based Survey

Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in a wide range of tasks, such as image recognition, machine translation, and self-driving cars. In several fields the considerable improvement in the computing hardware and the increasing need for big data analytics has boosted DL work. In recent years physiological signal processing has strongly benefited from deep learning. In general, there is an exponential increase in the number of studies concerning the processing of electromyographic (EMG) signals using DL methods. This phenomenon is mostly explained by the current limitation of myoelectric controlled prostheses as well as the recent release of large EMG recording datasets, e.g. Ninapro. Such a growing trend has inspired us to seek and review recent papers focusing on processing EMG signals using DL methods. Referring to the Scopus database, a systematic literature search of papers published between January 2014 and March 2019 was carried out, and sixty-five papers were chosen for review after a full text analysis. The bibliometric research revealed that the reviewed papers can be grouped in four main categories according to the final application of the EMG signal analysis: Hand Gesture Classification, Speech and Emotion Classification, Sleep Stage Classification and Other Applications. The review process also confirmed the increasing trend in terms of published papers, the number of papers published in 2018 is indeed four times the amount of papers published the year before. As expected, most of the analyzed papers (≈60 %) concern the identification of hand gestures, thus supporting our hypothesis. Finally, it is worth reporting that the convolutional neural network (CNN) is the most used topology among the several involved DL architectures, in fact, the sixty percent approximately of the reviewed articles consider a CNN