To build a python project of gender and age detector that can approximately guess the gender and age of the person in the image using OpenCV on the Adience dataset.
In this Python Project, we will approximately identify the gender and age of a person from the image. We will use the models trained by Tal Hassner and Gil Levi. The predicted gender may be one of ‘Male’ and ‘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 the image because of factors like makeup, lighting, obstructions, and facial expressions. And so, we make this as a classification problem instead of making it one of regression problem.
Computer Vision is the field of study that enables computers to see and identify digital images and videos as a human would. The challenges it faces largely follow from the limited understanding of biological vision. Computer Vision involves acquiring, processing, analyzing, and understanding digital images to extract high-dimensional data from the real world in order to generate symbolic or numerical information which can then be used to make decisions. The process often includes practices like object recognition, video tracking, motion estimation, and image restoration.
OpenCV is short for Open Source Computer Vision. Intuitively by the name, it is an open-source Computer Vision and Machine Learning library. This library is capable of processing real-time image and video while also boasting analytical capabilities. It supports the Deep Learning frameworks, Tensor flow, Caffe, and PyTorch. To go about the python project, we’ll:
- Detect faces
- Classify into Male/Female
- Classify into one of the 8 age ranges
- Put the results on the image and display it
Additional python libraries required for this project are as below :
- OpenCV (cv2) Other packages we’ll be needing come as part of the standard Python library.
- opencv_face_detector.pbtxt
- opencv_face_detector_uint8.pb
- age_deploy.prototxt
- age_net.caffemodel
- gender_deploy.prototxt
- gender_net.caffemodel
- a few pictures to try the project on For face detection, we have 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.
- First we have to clone our repository for the files and images we need.
- Using ‘cd’ command we change our directory to AGD folder.
- Download pretrained data and unzip it to make availability of some caffe models and deploy prototxts in it.
- Then we need to import python libraries like OpenCV, math etc..,
- For face, age, and gender, initialize protocol buffer and model.
- Initialize the mean values for the model and the lists of age ranges and genders to classify from.
- Let’s make a call to the getFaceBox() function with the faceNet and frame parameters, and what this returns, we will store in the names face and bboxes. Here, net is faceNet- this model is the DNN Face Detector .
- Create a shallow copy of frame and get its height and width.
- Create a blob from the shallow copy.
- Set the input and make a forward pass to the network.
- bboxes is an empty list now.Define the confidence (between 0 and 1). Wherever we find the confidence greater than the confidence threshold, which is 0.7, we get the x1, y1, x2, and y2 coordinates and append a list of those to bboxes.
- Then, we put up rectangles on the image for each such list of coordinates and return two things: the shallow copy and the list of bboxes.
- But if there are indeed bboxes, for each of those, we define the face, create a 4-dimensional blob from the image. In doing this, we scale it, resize it, and pass in the mean values.
- We feed the input and give the network a forward pass to get the confidence of the two class. Whichever is higher, that is the gender of the person in the picture.
- Then, we do the same thing for age.
- At last the input image is read, sends to the age gender detector function and display it with imshow().
- Uploading the Data
- Importing and downloading the pre-trained models
- Importing required python libraries
- Functions for Detecting Faces
- Defining the variables of weights and architectures for face, age, and gender detection models
- Loading the Models
- Framing , Age-Gender detecting functions
- Function to read , detect , and output the result
We tackled the estimation of age and gender detection of real-world face images. We posed the task as a detecting problem and as such, train the model with a estimation-based loss function as training targets. Our proposed model is originally pretrained on age and gender labelled large-scale Adience dataset. Finally, we use the original dataset (Adience benchmark of unfiltered faces for age and gender detection) to fine-tune this model. The robust image preprocessing algorithm, handles some of the variability observed in typical unfiltered real-world faces, and this confirms the model applicability for age and gender detection. Finally, we successfully done with the detecting accuracy on Adience dataset for age and gender; our proposed method achieves the state-of-the-art performance, in both age and gender detection, significantly outperforming the existing models. For future works, we will consider a deeper CNN architecture and a more robust image processing algorithm for exact age and gender estimation. Also, the apparent age estimation of human’s face will be interesting research to investigate in the future