Entropy Volumes for Viewpoint Independent Gait Recognition

Gait as biometrics has been widely used for human identi cation. However, direction changes cause di culties for most of the gait recognition systems, due to appearance changes. This study presents an e cient multi-view gait recognition method that allows curved trajectories on completely unconstrained paths for in- door environments. Our method is based on volumet- ric reconstructions of humans, aligned along their way. A new gait descriptor, termed as Gait Entropy Vol- ume (GEnV), is also proposed. GEnV focuses on cap- turing 3D dynamical information of walking humans through the concept of entropy. Our approach does not require the sequence to be split into gait cycles. A GEnV based signature is computed on the basis of the previous 3D gait volumes. Each signature is clas- si ed by a Support Vector Machine, and a majority voting policy is used to smooth and reinforce the clas- si cations results. The proposed approach is experimen- tally validated on the \AVA Multi-View Gait Dataset (AVAMVG)" and on the \Kyushu University 4D Gait Database (KY4D)". The results show that this new ap- proach achieves promising results in the problem of gait recognition on unconstrained paths

The AVA Multi-View Dataset for Gait Recognition

In this paper, we introduce a new multi-view dataset for gait recognition. The dataset was recorded in an indoor scenario, using six convergent cameras setup to produce multi-view videos, where each video depicts a walking human. Each sequence contains at least 3 complete gait cycles. The dataset contains videos of 20 walking persons with a large variety of body size, who walk along straight and curved paths. The multi-view videos have been processed to produce foreground silhouettes. To validate our dataset, we have extended some appearance-based 2D gait recognition methods to work with 3D data, obtaining very encouraging results. The dataset, as well as camera calibration information, is freely available for research purpose

Performance analysis of gait recognition with large perspective distortion

In real security scenarios, gait data may be highly distorted due to perspective effects and there may be significant change in appearance, orientation and occlusion between different measurements. To deal with this problem, a new identification technique is proposed by reconstructing 3D models of the walking subject, which are then used to identify subject images from an arbitrary camera. 3D models in one gait cycle are aligned to match silhouettes in a 2D gait cycle by estimating the positions of a 3D and 2D gait cycles in a 3D space. This allows the gait data in a gallery and probe share the same appearance, perspective and occlusion. Generic Fourier Descriptors are used as gait features. The performance is evaluated using a new collected dataset of 17 subjects walking in a narrow walkway. A Correct Classification Rate of 98:8% is achieved. This high recognition rate has still been achieved using a modest number of features. The analysis indicate that the technique can handle truncated gait cycles of different length and is insensitive to noisy silhouettes. However, calibration errors have a negative impact upon recognition performance

Gait Recognition

Gait recognition has received increasing attention as a remote biometric identification technology, i.e. it can achieve identification at the long distance that few other identification technologies can work. It shows enormous potential to apply in the field of criminal investigation, medical treatment, identity recognition, human‐computer interaction and so on. In this chapter, we introduce the state‐of‐the‐art gait recognition techniques, which include 3D‐based and 2D‐based methods, in the first part. And considering the advantages of 3D‐based methods, their related datasets are introduced as well as our gait database with both 2D silhouette images and 3D joints information in the second part. Given our gait dataset, a human walking model and the corresponding static and dynamic feature extraction are presented, which are verified to be view‐invariant, in the third part. And some gait‐based applications are introduced

A practical technique for gait recognition on curved and straight trajectories

Many studies show the effectiveness of gait in surveillance and access control scenarios. However, appearance changes due to walking direction changes impose a challenge for gait recognition techniques that assume people only walk in a straight line. In this paper, the effect of walking along straight and curved path is studied by proposing a practical technique which is based on the three key frames in the start, middle and end of the gait cycle. The position of these frames is estimated in 3D space which is then used to estimate the local walking direction in the first and second part of the cycle. The technique used 3D volume sequences of the people to adapt to changes in the walking direction. The performance is evaluated using a newly collected dataset and the Kyushu University 4D Gait Dataset, containing people walking in straight lines and curves. With the proposed technique, we obtain a correct classification rate of 98% for matching straight with straight walking and 81% for matching straight with curved walking averaged over both datasets. The variation in walking patterns when a person walks along a straight or curved path is most likely to be responsible for the difference. In support of this, the recognition rate when matching curved with curved walking is 99% on our dataset

A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition

Human gait recognition (HGR) shows high importance in the area of video surveillance due to remote access and security threats. HGR is a technique commonly used for the identification of human style in daily life. However, many typical situations like change of clothes condition and variation in view angles degrade the system performance. Lately, different machine learning (ML) techniques have been introduced for video surveillance which gives promising results among which deep learning (DL) shows best performance in complex scenarios. In this article, an integrated framework is proposed for HGR using deep neural network and fuzzy entropy controlled skewness (FEcS) approach. The proposed technique works in two phases: In the first phase, deep convolutional neural network (DCNN) features are extracted by pre-trained CNN models (VGG19 and AlexNet) and their information is mixed by parallel fusion approach. In the second phase, entropy and skewness vectors are calculated from fused feature vector (FV) to select best subsets of features by suggested FEcS approach. The best subsets of picked features are finally fed to multiple classifiers and finest one is chosen on the basis of accuracy value. The experiments were carried out on four well-known datasets, namely, AVAMVG gait, CASIA A, B and C. The achieved accuracy of each dataset was 99.8, 99.7, 93.3 and 92.2%, respectively. Therefore, the obtained overall recognition results lead to conclude that the proposed system is very promising

GaitFM: Fine-grained Motion Representation for Gait Recognition

Gait recognition aims at identifying individual-specific walking patterns, which is highly dependent on the observation of the different periodic movements of each body part. However, most existing methods treat each part equally and neglect the data redundancy due to the high sampling rate of gait sequences. In this work, we propose a fine-grained motion representation network (GaitFM) to improve gait recognition performance in three aspects. First, a fine-grained part sequence learning (FPSL) module is designed to explore part-independent spatio-temporal representations. Secondly, a frame-wise compression strategy, called local motion aggregation (LMA), is used to enhance motion variations. Finally, a weighted generalized mean pooling (WGeM) layer works to adaptively keep more discriminative information in the spatial downsampling. Experiments on two public datasets, CASIA-B and OUMVLP, show that our approach reaches state-of-the-art performances. On the CASIA-B dataset, our method achieves rank-1 accuracies of 98.0%, 95.7% and 87.9% for normal walking, walking with a bag and walking with a coat, respectively. On the OUMVLP dataset, our method achieved a rank-1 accuracy of 90.5%

Gait recognition from multiple view-points

A la finalización de la tesis, la principal conclusión que se extrae es que la forma de andar permite identificar a las personas con una buena precisión (superior al 90 por ciento y llegando al 99 por ciento en determinados casos). Centrándonos en los diferentes enfoques desarrollados, el método basado en características extraídas a mano está especialmente indicado para bases de datos pequeñas en cuanto a número de muestras, ya que obtiene una buena precisión necesitando pocos datos de entrenamiento. Por otro lado, la aproximación basada en deep learning permite obtener buenos resultados para bases de datos grandes con la ventaja de que el tamaño de entrada puede ser muy pequeño, permitiendo una ejecución muy rápida. El enfoque incremental está especialmente indicado para entornos en los que se requieran añadir nuevos sujetos al sistema sin tener que entrenar el método de nuevo debido a los altos costes de tiempo y energía. Por último, el estudio de consumo nos ha permitido definir una serie de recomendaciones para poder minimizar el consumo de energía durante el entrenamiento de las redes profundas sin penalizar la precisión de las mismas. Fecha de lectura de Tesis Doctoral: 14 de diciembre 2018.Arquitectura de Computadores Resumen tesis: La identificación automática de personas está ganando mucha importancia en los últimos años ya que se puede aplicar en entornos que deben ser seguros (aeropuertos, centrales nucleares, etc) para agilizar todos los procesos de acceso. La mayoría de soluciones desarrolladas para este problema se basan en un amplio abanico de características físicas de los sujetos, como pueden ser el iris, la huella dactilar o la cara. Sin embargo, este tipo de técnicas tienen una serie de limitaciones ya que requieren la colaboración por parte del sujeto a identificar o bien son muy sensibles a cambios en la apariencia. Sin embargo, el reconocimiento del paso es una forma no invasiva de implementar estos controles de seguridad y, adicionalmente, no necesita la colaboración del sujeto. Además, es robusto frente a cambios en la apariencia del individuo ya que se centra en el movimiento. El objetivo principal de esta tesis es desarrollar un nuevo método para la identificación de personas a partir de la forma de caminar en entornos de múltiples vistas. Como entrada usamos el flujo óptico que proporciona una información muy rica sobre el movimiento del sujeto mientras camina. Para cumplir este objetivo, se han desarrollado dos técnicas diferentes: una basada en un enfoque tradicional de visión por computador donde se extraen manualmente características que definen al sujeto y, una segunda aproximación basada en aprendizaje profundo (deep learning) donde el propio método extrae sus características y las clasifica automáticamente. Además, para este último enfoque, se ha desarrollado una implementación basada en aprendizaje incremental para añadir nuevas clases sin entrenar el modelo desde cero y, un estudio energético para optimizar el consumo de energía durante el entrenamiento

Gait Recognition: Databases, Representations, and Applications

There has been considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: there is now a plethora of technique and data which continue to show that a person’s walking is indeed unique. Gait recognition is a behavioural biometric which is available even at a distance from a camera when other biometrics may be occluded, obscured or suffering from insufficient image resolution (e.g. a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its non-invasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private spaces. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations are described in the following sections.Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases which incorporate subject diversities and/or rich covariate factors.Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model-free (appearance-based) approaches and model-based approaches. In particular, silhouette-based model-free approaches predominate in recent studies and many have been proposed and are described in detail.Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, time elapse; and OU-ISIR LP with more than 4,000 subjects. Since gait recognition is suitable for criminal investigation applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition