Using image data, predict the gender and age range of an individual in Python.

Using image data, predict the gender and age range of an individual in Python. Test the data science model using your own image.

Dataset used:

Adience Facial dataset for gender and age prediction for unfiltered faces

The data included in this collection is intended to be as true as possible to the challenges of real-world imaging conditions. In particular, it attempts to capture all the variations in appearance, noise, pose, lighting and more, that can be expected of images taken without careful preparation or posing.

The sources of the images included in our set are Flickr albums, assembled by automatic upload from iPhone5 (or later) smart-phone devices, and released by their authors to the general public under the Creative Commons (CC) license. This data set was used in the paper Age and Gender Estimation of Unfiltered Faces.

Total number of photos: 26,580

Total number of subjects: 2,284

Number of age groups / labels: 8 (0-2, 4-6, 8-13, 15-20, 25-32, 38-43, 48-53, 60-)

Gender labels: Yes

In the wild: Yes

Subject labels: Yes

CNN Architecture:

We have used a very simple convolutional neural network architecture, similar to the CaffeNet and AlexNet. The network uses 3 convolutional layers, 2 fully connected layers and a final output layer. The details of the layers are given below.

Conv1 : The first convolutional layer has 96 nodes of kernel size 7.

Conv2 : The second conv layer has 256 nodes with kernel size 5.

Conv3 : The third conv layer has 384 nodes with kernel size 3.

The two fully connected layers have 512 nodes each.

Adience dataset is used for training the model.

Gender Prediction

They have framed Gender Prediction as a classification problem. The output layer in the gender prediction network is of type softmax with 2 nodes indicating the two classes “Male” and “Female”.

Age Prediction

Ideally, Age Prediction should be approached as a Regression problem since we are expecting a real number as the output. However, estimating age accurately using regression is challenging. Even humans cannot accurately predict the age based on looking at a person. Ho
wever, we have an idea of whether they are in their 20s or in their 30s. Because of this reason, it is wise to frame this problem as a classification problem where we try to estimate the age group the person is in. For example, age in the range of 0-2 is a single class, 4-6 is another class and so on.

The Adience dataset has 8 classes divided into the following age groups [(0 – 2), (4 – 6), (8 – 12), (15 – 20), (25 – 32), (38 – 43), (48 – 53), (60 – 100)]. Thus, the age prediction network has 8 nodes in the final softmax layer indicating the mentioned age ranges.

It should be kept in mind that Age prediction from a single image is not a very easy problem to solve as the perceived age depends on a lot of factors and people of the same age may look pretty different in various parts of the world. Also, people try very hard to hide their real age by some makeup or camera filters.

Some Examples:

Supporting Files:

Files