Gait features fusion for efficient automatic age classification

Mansouri, Nabila; Issa, Mohammed Aouled; Jemâa, Yousra Ben

doi:10.1049/iet-cvi.2017.0055

Cited by 17 publications

(6 citation statements)

References 48 publications

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“…It is clearly seen that GPM gave best results to that of existing descriptors. The fusion of descriptors [13] gave the best outcomes when compared to that of the individual descriptors. OULP dataset produced an excellent recognition rate in the case of gender.…”

Section: Discussionmentioning

confidence: 99%

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Recognition of Gender using Gait Energy Image Projections Based on Random Forest Classifier

Hema*¹,

Babulu²,

Balaji³

2019

IJITEE

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Identification of gender is a very fascinating criterion in the present day scenario. Especially, in the surveillance applications, gender recognition is very beneficial. With the use of face, speech, voice and gait, the gender of a person can be determined. Non-contact, non-invasive and easily acquired at distance, gait analysis has attracted the interest of many researchers in the classification of gender. For the identification of gender, 2 stages of the methodology are used in our proposed work. A new descriptor called Gait energy image projection model(GPM) is proposed which highlights all the gender-related parameters. In the second stage of methodology, proposed descriptor GPM is fused with already existing descriptors like GEI and FED for enhanced performance. For classifying the gender, an Ensemble classifier called Random Forests is applied to the individual and fused descriptors and the results are evaluated. Two datasets are used for experimentation namely CASIA B and OU-ISIR datasets which are standard datasets for person identification and different performance metrics such as accuracy, precision, recall and error rate are evaluated.

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Section: Discussionmentioning

confidence: 99%

“…As a result, the results produced from this noisy information may not be correct. So, gait analysis is considered as the best suitable solution for gender prediction [10].…”

Section: Fig 1 Gender Taxonomymentioning

confidence: 99%

Recognition of Gender using Gait Energy Image Projections Based on Random Forest Classifier

Hema*¹,

Babulu²,

Balaji³

2019

IJITEE

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“…As an improvement on SM, Mansouri et al [30] proposed SGF, a fusion of SM, GEI, and FED. They showed that the fusion-based descriptor performed better than any of the individual descriptors for age classification.…”

Section: ) Gait Age From Model-free Featuresmentioning

confidence: 99%

Learning Age From Gait: A Survey

et al. 2021

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Age is an important human attribute that needs to be determined for various purposes, including security, health, human identification, and law enforcement. Hence, there is an increasing research interest in automatic age estimation using biometric traits such as face and gait. In recent years, gait analysis has received growing attention due to the pervasive nature of video surveillance. Gait signals that measure the manner of walking can be obtained using vision and sensor-based techniques. Individual gait patterns obtainable from videos, images, or sensors are shown unconsciously and are not easily obscured. Additionally, gait signals can be obtained unobtrusively with cameras placed at a long distance because gait does not require high-resolution images. However, the extraction of age-associated gait features is a challenging task due to various gait covariates. These covariates include clothing and view changes for visionbased gait; walking slope and footwear for sensor-based gait. This paper provides a survey of scientific literature on age estimation gait features. We focus on the approaches to extracting age-associated gait features, namely, vision-based and sensor-based approaches, how they may be affected by the different covariates, and domain-specific applications. To make this work useful for as wide of an audience as possible, we also include discussions on key topics such as existing datasets, evaluation strategies, and open challenges that should be addressed in the future.

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“…İnsanların yürüyüş tarzlarından cinsiyetlerinin tanınması ile ilgili de birçok farklı çalışma vardır (Barclay et al 1978, Barra et al 2019, El-Alfy and Binsaadoon 2019, Xuelong Li et al 2008. Benzer şekilde insanların yürüyüşlerinden kaç yaşında olduğunu tahmin eden farklı çalışmalar da vardır (Xiang , Makihara et al 2011, Mansouri et al 2017, Nabila et al 2017.…”

Section: Literatür Taramasıunclassified

Akıllı Telefonların İvmeölçer Sensörü Yardımıyla Yürüyüş Deseni Analizi

Aydemir¹,

Karslioğlu²

2021

Afyon Kocatepe University Journal of Sciences and Engineering

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Spor alanlarında insan hareketlerini ölçme yeteneği performans ölçüm ve gelişimi için önemli konular arasındadır. Bu durum aynı zamanda klinik değerlendirmelerin de önemli bir parçasıdır. Özellikle elektromanyetik sistemler insan hareketlerini değerlendirmek için en yaygın kullanılan yöntemler arasında yer alır. Buradaki çalışmada 100 metre uzunluğunda bir koridorda 50 farklı kişinin yürüme verileri kullanılmıştır. Yürüme verileri akıllı telefon için geliştirilen bir yazılım ile ivmeölçer sensöründen elde edilmiştir. Verilere üç boyutlu Local Binary Pattern (LBP) yöntemi uygulanmış ve toplam 768 öznitelik çıkarılmıştır. Farklı sınıflandırma algoritmaları ile testler yapılmış ve Subspace KNN ile %97,2 başarılı sınıflandırma elde edilmiştir. Cinsiyete göre yapılan sınıflandırmada ise %99,7 başarılı sınıflandırma elde edilmiştir. Bu yöntem ile yürüme bozukluğu tespitinde yüksek maliyetli cihazlar yerine daha ekonomik yöntemler geliştirileceği düşünülmektedir.

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Gait features fusion for efficient automatic age classification

Cited by 17 publications

References 48 publications

Recognition of Gender using Gait Energy Image Projections Based on Random Forest Classifier

Recognition of Gender using Gait Energy Image Projections Based on Random Forest Classifier

Learning Age From Gait: A Survey

Akıllı Telefonların İvmeölçer Sensörü Yardımıyla Yürüyüş Deseni Analizi

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