Initial coding for detecting Age ,Emotion And gender of a person.

[ShahkarHassan]

The algorithm works in such a way that it performs physical analysis by detecting the face, age, and gender of a particular person. Face Detection - helps in understanding customer behavior and experience. Age Detection - helps in establishing an efficient Stock Management System (by determining the size of items usually purchased and create their availability in the store). Gender Detection - helps in comprehending which gender has more interest in the store and rearrange the stock according to that.

[ShahkarHassan]

Datasets were taken from:

Age and Gender Detection Dataset:

Data was Uploaded , Trained , preprocesed and then Evaluated. The accuracy we got was 60-70% for Age Detection . and 80% for Gender detection by this Model.

The Code is under: Age_and_Gender_Prediction.ipynb

Emotion Detection Dataset: https://www.kaggle.com/shawon10/ckplus

Data was Uploaded , Trained , preprocesed and then Evaluated. The accuracy we got was 100% training accuracy and 100% validation accuracy.

The Code is under: Emotion_Detection.ipynb

[MeharunisaAshraf]

I implemented another code of Facial recognition which is a project of PYPI . I ran that and analyzed the results according to our needs and studied the requirements and configurations of Deepface.

• Deepface.ipynb is uploaded.

• If you're working on a Local Machine, install the dependencies from your terminal with -

pip install -r requirements.txt - Run -Download the Repository. -Install the Dependencies in your system through your terminal/cmd using - -pip install -r requirements. -Open DeepFace.ipynb using any ipynb based console. (Eg- Colab, Jupyter Lab etc)

• For Live Facial Recognition and Verification,

run the following command in your Terminal/cmd - python deep_face.py