Predicting Age and Gender Using AlexNet

• Published in: TEM Journal 2023-02, Vol.12 (1), p.512-518
• Main Authors: Abood, Qaswaa Khaled, AL-Jibory, Farah khiled
• Format: Article
• Language: English
• Subjects: Algorithms; Biomarkers; Biometric recognition systems; Biometrics; Chronology; Education and training; Electronic information storage and retrieval; Forehead; Gender; Machine learning; Parameter estimation; Parameter identification; Security systems
• ISSN: 2217-8309; 2217-8333
• DOI: 10.18421/TEM121-61

Due to the availability of technology stemming from in-depth research in this sector and the drawbacks of other identifying methods, biometrics has drawn maximum attention and established itself as the most reliable alternative for recognition in recent years. Efforts are still being made to develop a user-friendly system that is up to par with security-system requirements and yields more reliable outcomes while safeguarding assets and ensuring privacy. Human age estimation and Gender identification are both challenging endeavours. Biomarkers and methods for determining biological age and gender have been extensively researched, and each has advantages and disadvantages. Facial-image-based positioning is crucial for many applications, including safety and security systems, border control, human engagement in sophisticated ambient analytics, and biometric identification. Determining a person's age and gender is a complex study method. With the advent of deep learning, the study of face systems has been completely transformed, and estimation accuracy is a crucial parameter for evaluating algorithms and their efficacy in predicting absolute ages. The UTKFace dataset, which serves as the backbone of the face estimating system, was used to assess the method. The eyes, cheeks, nose, lips, and forehead provide the foundation of this function. AlexNet achieves a 98% accuracy rate across its lifespan of system results.