UTKFace Dataset is a large-scale face dataset with a long age span (range from 0 to 116 years old). The objective is to classify each face based on gender using CNNs on Tensorflow 2.x and then, use OpenCV & Haar Cascade File to check the gender in real-time.
UTKFace Dataset Total Images:
The Dataset consists of over 20,000 Face Images
The images cover large variations in pose, facial expression, illumination, resolution, etc. This dataset could be used on a variety of tasks, e.g., face detection, age estimation, gender detection, landmark localization, etc.
You will be using the Aligned & Cropped Faces Dataset from the UTKFace Dataset.
Dataset available at here
Tasks 1 to be performed
.ipynb file 1
Do you get the same results if you run the Notebook multiple times without changing any parameters?
What is the effect of adding more neurons to each Conv2D layer?
What happens if we manipulate the value of dropout?
What is the effect of adding more activation layers to the network?
What is the purpose of MaxPooling2D layer?
Does manipulating the learning rate affect the model? Justify your answer.
Tasks 2 to be performed
.ipynb file 2
Try improving the model and re-code the program from scratch without looking too much at this source code
Write up a summary explaining how your program works
Tasks 3 to be performed
.ipynb file 3
Import Required Libraries
Prepare the dataset for the model
Develop CNN model for recognizing the gender
Analyze the model summary
Fit the basic CNN model
Predict the Gender of the uploaded image
Use OpenCV and Haar Cascade File to check the gender in Real-time
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