To build a live age and gender detector that can guesstimate the gender and age of the user in a picture or through webcam.
In this Python Project, I had used Deep Learning to accurately identify the gender and age of a person from a single image of a face. The predicted gender may be ‘Male’ or ‘Female’, and the predicted age may be one of the following ranges- (0 – 2), (4 – 6), (8 – 12), (15 – 20), (25 – 32), (38 – 43), (48 – 53), (60 – 100) (8 nodes in the final softmax layer). It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions.
For this python project, I had used the Adience dataset, which can be found at https://www.kaggle.com/ttungl/adience-benchmark-gender-and-age-classification. This dataset serves as a benchmark for face photos and is inclusive of various real-world imaging conditions like noise, lighting, pose, and appearance. The images have been collected from Flickr albums and distributed under the Creative Commons (CC) license. It has a total of 26,580 photos of 2,284 subjects in eight age ranges (as mentioned above) and is about 1GB in size. The models I used had been trained on this dataset.
The training folder contains a .pb file- this is a protobuf file (protocol buffer); it holds the graph definition and the trained weights of the model. We can use this to run the trained model. And while a .pb file holds the protobuf in binary format, one with the .pbtxt extension holds it in text format. These are TensorFlow files. For age and gender, the .prototxt files describe the network configuration and the .caffemodel file defines the internal states of the parameters of the layers.
- Use Command:
python detect.py --image <image_name>
Note: The Image should be present in same folder where all the files are present
- Detecting Gender and Age of face through webcam Use Command :
python detect.py
- Press Ctrl + C to stop the program execution.