AGE AND GENDER DETECTION USING DEEP LEARNING.pptx

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1. AGE AND GENDER DETECTION USING DEEP LEARNING Karthik SS S3MCA

2. INDRODUCTION Human Classification is an age-old procedure and being done in various fields and technology such as biometrics, forensics sciences, Image processing, Identification system, etc. With the development of Artificial Intelligence and techniques such as Neural Network and Deep Learning, it has become increasingly easier to classify human. These new technologies help identification, classification of Individuals without the need of another professional or Individual records. Also Being immensely fast, these technologies can classify millions of individuals way faster than a professional. Human Facial Image Processing provides many clues and cues applicable to industries such as security, entertainment, etc. . Human Face can provide immense amount of information like their emotional state, slightest agreement or disagreement, irony or anger, etc. This is the reason why faces have been long research topic in psychology . This data (or in our case digital data) is very valuable as they help recognition, selection or identification of individual according to the requirement. Age and Gender Detection can alone provide a lot of information to places such as recruitment team of organizations, Verification of ID cards, example: Voter ID cards which millions of individual uses to cast their vote at the time of election, etc. Human Facial Image processing eases the task of finding ineligible or counterfeit individuals

3. Aim of this project: In this model, we attempt to propose a form validator to validate a gender and age range that’s reflected from user photo, based on an automatic age and gender classification using CNN and evaluate it using real persons photos dataset. We follow the successful example laid down by recent face recognition systems: Face recognition techniques described in the last few years have shown that tremendous progress can be made by the use of deep convolutional neural networks (CNN).

4. Module Description: Image Recognition: Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework specifically designed for Convolutional Neural Networks (CNNs). Caffe is closely associated with CNNs and is designed to facilitate the creation, training, and deployment of convolutional neural networks for tasks related to computer vision and image processing.

5. Pre Processing: The captured gestures is resized, converted to a greyscale image and the noise is filtered to achieve prediction with high accuracy. Classification: The classification and prediction are done using a convolutional neural network (CNN).

6. Prediction: The converted real-time text appears in the text box. Generated text will be the gender and age of the specified image.

7. Process Flow Diagram Image Acquisition Face Detection Feature Extraction Convolutional Neural Network Final Output Camera

8. Screenshots Input Design Output Design

9. Conclusion We presented the accuracy of many classifiers in this paper. CNN is primarily employed as a learning function for feature extraction, which allows it to determine age and gender. We use 5 hidden layers and 5-fold cross validation to produce a more accurate result. Combination of feature extractions like frequency, and color with different classifiers will add accuracy to these applications.

10. Future Enhancement The model can be used in different field such as retail management sector, cosmetic industry and security area prominently. It is utilized in a variety of ways in retail, from security to ads. From sending personalized adverts to identifiable customers to gender identification. Model can be used in entertainment field also , Content Recommendation: Streaming services can use age and gender information to recommend content that is more likely to be of interest to the user based on their demographic profile. Personalized user interface: Systems that can detect age and gender can adapt their user interfaces to be more user-friendly for different demographic groups

11. References Arora, Shefali, and M. P. S. Bhatia, “A Robust Approach for Gender Recognition Using Deep Learning,” In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-6, 2018 Wang, Xiaofeng , Azliza Mohd Ali, and Plamen Angelov, “Gender and age classification of human faces for automatic detection of anomalous human behaviour.” In 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), pp. 1-6, 2017. Gender and Age Detection using Deep Learning Utkarsha Kumbhar *, Prof. A. S. Shingare .

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