Human Gait Recognition using Deep Convolutional Neural Network

Nithyakani, P; Shanthini, A.; Ponsam, G. Godwin

doi:10.1109/iccct2.2019.8824836

Cited by 22 publications

(11 citation statements)

References 8 publications

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“…Gait identification with low-resolution cameras is difficult, according to the work in Ref. [13]. They suggested a deep CNN network that extracts features, inputs Gait Energy Image (GEI), and is trained by a neural network.…”

Section: Deep Learning-based Model-free Gait Methodsmentioning

confidence: 99%

Protection of gait data set for preserving its privacy in deep learning pipeline

Parashar

Shekhawat

2022

IET Biometrics

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Human gait is a biometric that is being used in security systems because it is unique for each individual and helps recognise one from a distance without any intervention. To develop such a system, one needs a comprehensive data set specific to the application. If this data set somehow falls in the hands of rogue elements, they can easily access the secured system developed based on the data set. Thus, the protection of the gait data set becomes essential. It has been learnt that systems using deep learning are easily prone to hacking. Hence, maintaining the privacy of gait data sets in the deep learning pipeline becomes more difficult due to adversarial attacks or unauthorised access to the data set. One of the popular techniques for stopping access to the data set is using anonymisation. A reversible gait anonymisation pipeline that modifies gait geometry by morphing the images, that is, texture modifications, is proposed. Such modified data prevent hackers from making use of the data set for adversarial attacks. Nine layers were proposedto effect geometrical modifications, and a fixed gait texture template is used for morphing. Both these modify the gait data set so that any authentic person cannot be identified while maintaining the naturalness of the gait. The proposed method is evaluated using the similarity index as well as the recognition rate. The impact of various geometrical and texture modifications on silhouettes have been investigated to identify the modifications. The crowdsourcing and machine learning experiments were performed on the silhouette for this purpose. The obtained results in both types of experiments showed that texture modification has a stronger impact on the level of privacy protection than geometry shape modifications. In these experiments, the similarity index achieved is above 99%. These findings open new research directions regarding the adversarial attacks and privacy protection related to gait recognition data sets.

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Section: Deep Learning-based Model-free Gait Methodsmentioning

confidence: 99%

Protection of gait data set for preserving its privacy in deep learning pipeline

Parashar

Shekhawat

2022

IET Biometrics

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“…A few other studies were also conducted for HGR, such as PoseGait and the CNN model [13], a multichannel CNN architecture [38], GEI and CNN architecture [53], and RCNN based gait recognition [54], among others [55]. The methods mentioned focused on the middle step, for example, GEI, handcrafted features, and CNN.…”

Section: Related Workmentioning

confidence: 99%

Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

Saleem

Khan

Alhaisoni

et al. 2021

Sensors

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Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differing views is a significant difficulty in HGR. Many techniques have been introduced in the literature for HGR using conventional and deep learning techniques. However, the traditional methods are not suitable for large datasets. Therefore, a new framework is proposed for human gait recognition using deep learning and best feature selection. The proposed framework includes data augmentation, feature extraction, feature selection, feature fusion, and classification. In the augmentation step, three flip operations were used. In the feature extraction step, two pre-trained models were employed, Inception-ResNet-V2 and NASNet Mobile. Both models were fine-tuned and trained using transfer learning on the CASIA B gait dataset. The features of the selected deep models were optimized using a modified three-step whale optimization algorithm and the best features were chosen. The selected best features were fused using the modified mean absolute deviation extended serial fusion (MDeSF) approach. Then, the final classification was performed using several classification algorithms. The experimental process was conducted on the entire CASIA B dataset and achieved an average accuracy of 89.0. Comparison with existing techniques showed an improvement in accuracy, recall rate, and computational time.

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“…2) Convolutional Neural Networks (CNNs): CNN is a typical DL model which uses different levels of abstraction to learn the hierarchical representations of patterns existing in the dataset. CNN have been extensively utilised to classify and recognize humans based on various gait parameters i.e., footsteps [46], gender and age [47], gait energy images [48], gestures [49] and freezing of gait [50] etc.…”

Section: Deep Learning Based Multi-modality Sensor Fusionmentioning

confidence: 99%

Gait Activity Classification Using Multi-Modality Sensor Fusion: A Deep Learning Approach

Yunas

Ozanyan

2021

IEEE Sensors J.

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Floor Sensors (FS) are used to capture information from the force induced on the contact surface by feet during gait. On the other hand, the Ambulatory Inertial Sensors (AIS) are used to capture the velocity, acceleration and orientation of the body during different activities. In this paper, fusion of the stated modalities is performed to overcome the challenge of gait classification from wearable sensors on the lower portion of human body not in contact with ground as in FS. Deep learning models are utilized for the automatic feature extraction of the ground reaction force obtained from a set of 116 FS and body movements from AIS attached at 3 different locations of lower body, which is novel. Spatio-temporal information of disproportionate inputs obtained from the two modalities is balanced and fused within deep learning network layers whilst reserving the categorical content for each gait activity. Our approach of fusion compensates the degradation in spatio-temporal accuracies in individual modalities and makes the overall classification outcomes more accurate. Further assessment of multi-modality based results show significant improvements in f-scores using different deep learning models i.e., LSTM (99.90%), 2D-CNN (88.73%), 1D-CNN (94.97%) and ANN (89.33%) respectively.

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Human Gait Recognition using Deep Convolutional Neural Network

Cited by 22 publications

References 8 publications

Protection of gait data set for preserving its privacy in deep learning pipeline

Protection of gait data set for preserving its privacy in deep learning pipeline

Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

Gait Activity Classification Using Multi-Modality Sensor Fusion: A Deep Learning Approach

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