Enhanced Human Identity and Gender Recognition from Gait Sequences using SVM and MDA

Saini, A. D.; Singh, Harpreet

doi:10.5120/21037-3358

Cited by 5 publications

(2 citation statements)

References 23 publications

(7 reference statements)

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“…Alka Saini et al [14] presented a similar gender classification approach along with the Multi linear discriminant analysis classifier for enhancing the gender classification. Zhang De et al [6] presented a gait based gender classification approach in which the extracted features of different view angles are combined using multi-view fusion.…”

Section: Related Workmentioning

confidence: 99%

An Effective reduction of Gait Recognition Time by using Gender Classification

Aasha

Sivaranjani

Sivakumari

2016

Proceedings of the International Conference on Advances in Information Communication Technology &Amp; Computing - AICTC '16

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This work investigates the possibility of utilizing gender classification in gait recognition to reduce search space time as gender classification reduces the total number of search subjects from database. In this paper, gender classification is performed by utilizing Sparse spatiotemporal features along with most effective features, more informative less effective features and shape features. The spatiotemporal interest points are detected by improved Harris corner detector which is simulated annealing optimized Harris corner detector. The spatiotemporal interest points among the video frames are treated as the central point around which a cuboid of pixel values are extracted and processed to form final descriptor. The most effective features and more informative less effective features are extracted by the cofactor entropy while the shape features are extracted using angular radial transform and FFT and the feature fusion is performed using BAT as in our previous works. Then the Sparse Multi-kernel Support Vector Machine (SM-SVM) is used for subject classification. Experimental results show that the proposed method provides efficient gender classification and thus reduces the gait recognition time.

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Section: Related Workmentioning

confidence: 99%

An Effective reduction of Gait Recognition Time by using Gender Classification

Aasha

Sivaranjani

Sivakumari

2016

Proceedings of the International Conference on Advances in Information Communication Technology &Amp; Computing - AICTC '16

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“…SVM is a popular choice of classifier in many recently proposed methods, e.g., [13], due to its robustness. Saini and Singh [14] proposed a gender recognition system using SVM and multi-linear discriminant analysis (MDA) as classifiers. Do et al [15] proposed a view-dependent gender classification system.…”

Section: Deterministic Algorithmmentioning

confidence: 99%

Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses

Leung,

Chan

2023

Sensors

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Image-based gender classification is very useful in many applications, such as intelligent surveillance, micromarketing, etc. One common approach is to adopt a machine learning algorithm to recognize the gender class of the captured subject based on spatio-temporal gait features extracted from the image. The image input can be generated from the video of the walking cycle, e.g., gait energy image (GEI). Recognition accuracy depends on the similarity of intra-class GEIs, as well as the dissimilarity of inter-class GEIs. However, we observe that, at some viewing angles, the GEIs of both gender classes are very similar. Moreover, the GEI does not exhibit a clear appearance of posture. We postulate that distinctive postures of the walking cycle can provide additional and valuable information for gender classification. This paper proposes a gender classification framework that exploits multiple inputs of the GEI and the characteristic poses of the walking cycle. The proposed framework is a cascade network that is capable of gradually learning the gait features from images acquired in multiple views. The cascade network contains a feature extractor and gender classifier. The multi-stream feature extractor network is trained to extract features from the multiple input images. Features are then fed to the classifier network, which is trained with ensemble learning. We evaluate and compare the performance of our proposed framework with state-of-the-art gait-based gender classification methods on benchmark datasets. The proposed framework outperforms other methods that only utilize a single input of the GEI or pose.

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