Age And Gender Detection Using Cnn
Abstract
Age and gender detection is a crucial technology with applications in surveillance, healthcare, personalized marketing, and social media analytics. By analyzing facial features, machine learning algorithms can efficiently classify individuals based on age group and gender, enhancing security measures and improving user experiences. Traditional methods often struggle with accuracy due to variations in facial expressions, lighting, and occlusions. However, deep learning techniques, especially Convolutional Neural Networks (CNNs), have demonstrated remarkable improvements in classification performance by automatically learning hierarchical feature representations. This paper provides an in-depth review of machine learning techniques applied to age and gender detection, evaluating their methodologies, datasets, and effectiveness. Furthermore, this study explores the challenges in implementing these models and highlights future research directions to improve real-world applicability and accuracy
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O. Agbo-Ajala and S. Viriri, “Deeply learned classifiers for age and gender predictions of unfiltered faces”, Sci. World J., vol 2020, pp. 1-12, Apr. 2020.
A. M. Bukar, H. Ugail and D. Connah, “Automatic age and gender classification using supervised appearance model”, J. Electron. Imag., vol. 25, no. 6, Aug. 2016.
J. H. Lee, Y. M. Chan, T. Y. Chen and C. S. Chen, "Joint estimation of age and gender from unconstrained face images using lightweight multi-task CNN for mobile applications", Proc. IEEE Conf. Multimedia Inf. Process. Retr. (MIPR), Apr. 2018.
S. Hosseini, S. H. Lee, H. J. Kwon, H. I. Koo and N. I. Cho, "Age and gender classification using wide convolutional neural network and Gaborfilter", Proc. Int. Workshop Adv. Image Technol., pp. 1-3, 2018.
E. Eidinger, R. Enbar and T. Hassner, "Age and Gender Estimation of UnfilteredFaces", 2170 IEEE Trans. Inf. FORENSICS Secur., vol. 9, no. 12, pp. 2170-2179, 2014
K. Zhang, C. Gao, L. Guo, M. Sun, S. Member and X. Yuan, “Age Group and GenderEstimation in the Wild with Deep RoR Architecture”, vol. XX, no. X, pp. 1-11, 2017
Li, M.; Zhou, Z. Improve computer- aided diagnosis with machine learning techniques using undiagnosed. IEEE Trans. Syst. Man. Cybern. Syst. 2007, 37, 1088–1098.
Gil Levi, Tal Hassner Age and Gender Classification using Convolutional Neural Networks2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2015.
Benyamin Ghojogh, Saeed Bagheri Shouraki, Hoda Mohammadzade, Ensieh Iranmehr·A Fusion-based Gender Recognition Method Using Facial Images17 Nov 2017·
Age and Gender Detection, Harcharan Kaur Universal group of institutes, lalru Punjab – India, 2017.
Gender and Age Classification in Camera Data, MASTER’S THESIS Be. Matúš Námešný Brno, Spring 2019
Age and Gender Classification using Multiple Convolutional Neural Network. Khaled Rahman Hassan1, Israa Hadi Ali2 1,2College of Information Technology, University of Babylon, Babylon, Iraq
H. Han, C. Otto, X. Liu and A.K. Jain, "Demographic estimation from face images: Human vs. machine performance", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 37, no. 6, pp. 1148-1161, June 2015.
Yaman Akbulut; Abdulkadir Şengür, Gender recognition from face images with deep learning Date Added to IEEE Xplore: 02 November 2017.
M. H. U. Yap, “Facial image processing for facial analysis,” Proceedings - International Carnahan Conference on Security Technology., vol. 1, 2010.
R. E. a. T. H. E. Eidinger, “Age and Gender Estimation of Unfiltered Faces,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 12, pp. 2170-2179, 2014.
Elham Arianasab, Mohsen Maadani, Abolfazl Gandomi, “A Neural Network Based Gender Detection Algorithm on Full-face Photograph,” 2015 2nd Interenation Conference on Knowledge Based Engineering and Innovation November 5-6, 2015, 978-1- 4673-6506-2/15-IEEE.
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Copyright (c) 2025 Turlapati Sai Prasanth, K. Amarendra
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