Welcome to use AbleSci to get your papers. Our English Version is under development. You can temporarily use Google to translate AbleSci. Quite easy! Thank you!

PEDESTRIAN GENDER RECOGNITION WITH HANDCRAFTED FEATURE ENSEMBLES

作者
Mehshan Ahad,Muhammad Fayyaz
出处
期刊:Azerbaijan journal of high performance computing [Azerbaijan State Oil and Industry University]
卷期号:4 (1): 60-90 被引量:1
标识
DOI:10.32010/26166127.2021.4.1.60.90
摘要

Human gender recognition is one the most challenging task in computer vision, especially in pedestrians, due to so much variation in human poses, video acquisition, illumination, occlusion, and human clothes, etc. In this article, we have considered gender recognition which is very important to be considered in video surveillance. To make the system automated to recognize the gender, we have provided a novel technique based on the extraction of features through different methodologies. Our technique consists of 4 steps a) preprocessing, b) feature extraction, c) feature fusion, d) classification. The exciting area is separated in the first step, which is the full body from the images. After that, images are divided into two halves on the ratio of 2:3 to acquire sets of upper body and lower body. In the second step, three handcrafted feature extractors, HOG, Gabor, and granulometry, extract the feature vectors using different score values. These feature vectors are fused to create one strong feature vector on which results are evaluated. Experiments are performed on full-body datasets to make the best configuration of features. The features are extracted through different feature extractors in different numbers to generate their feature vectors. Those features are fused to create a strong feature vector. This feature vector is then utilized for classification. For classification, SVM and KNN classifiers are used. Results are evaluated on five performance measures: Accuracy, Precision, Sensitivity, Specificity, and Area under the curve. The best results that have been acquired are on the upper body, which is 88.7% accuracy and 0.96 AUC. The results are compared with the existing methodologies, and hence it is concluded that the proposed method has significantly achieved higher results.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
1秒前
zzl发布了新的文献求助30
3秒前
李健的小迷弟应助Roseyy采纳,获得10
6秒前
谨慎秋寒发布了新的文献求助10
6秒前
9秒前
9秒前
10秒前
华仔应助black_cavalry采纳,获得10
12秒前
llllxj发布了新的文献求助10
12秒前
苏杨发布了新的文献求助10
13秒前
14秒前
16秒前
20秒前
暮光之城完成签到,获得积分10
22秒前
22秒前
科研老白完成签到,获得积分10
24秒前
24秒前
852应助Doc_Ocean采纳,获得10
24秒前
24秒前
25秒前
26秒前
27秒前
27秒前
顾矜应助楠D采纳,获得10
28秒前
陈哥完成签到,获得积分10
28秒前
huangyi完成签到,获得积分10
28秒前
兴奋的白梦完成签到,获得积分10
29秒前
30秒前
30秒前
汉堡包应助谨慎秋寒采纳,获得10
30秒前
乐乐应助qwer采纳,获得10
30秒前
奶油橘子完成签到,获得积分10
30秒前
30秒前
Ray完成签到,获得积分10
31秒前
在路上发布了新的文献求助10
31秒前
fffff发布了新的文献求助10
32秒前
阿斯顿发布了新的文献求助30
32秒前
33秒前
优秀冰真发布了新的文献求助10
33秒前
高分求助中
Zur Systematik zentralasiatischer Grünkröten (Bufo viridis - Komplex)(Amphibia: Salientia: Bufonidae) 1000
fib Bulletin 95. Fibre Reinforced Concrete: From Design to Structural Applications 400
Photoelectric Characteristics of Rare Earth Element Er Doped Molybdenum Selenide Thin Films 300
The statistical analysis in the era of big data 300
RF measurements of die and packages  300
Das Skelett des Höhlenlöwen (Panthera leo spelaea Goldfuss, 1810) aus Siegsdorf, Ldkr. Traunstein im Vergleich mit anderen Funden aus Deutschland und den Niederlanden 300
2023 JCO Orthodontic Practice Study Part 1: Trends 300
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 计算机科学 化学工程 复合材料 基因 物理化学 遗传学 催化作用 电极 量子力学 病理
热门帖子
关注 科研通微信公众号,转发送积分 1955955
求助须知:如何正确求助?哪些是违规求助? 1586705
关于积分的说明 3591987
捐赠科研通 1384074
什么是DOI,文献DOI怎么找? 738905
版权声明 515681
科研通“疑难数据库(出版商)”最低求助积分说明 378246