This is the Age and Gender prediction model built using Convolutional Neural Networks in Python. The model is built on the basis of Research Paper by Gil Levi and Tal Hassner. The neural architecture is built as written in the paper with slight modifications according to the dataset for better perfomance.
The dataset used are UTKFace Dataset and FairFace Dataset out of which Indian Race faces were seperated and the model was trained with.
This Repository contains the notebook used for building the Age model, the Gender model and the notebook used for testing thier performance. The Saved Pretrained Model folder contains the pretrained model of Age and Gender which could be used for testing and re-training purposes. The weights are saved in .h5 format and the model is saved in .json format.
- Tensorflow
pip install tensorflow - Numpy
pip install numpy - Keras
pip install keras - Pillow
pip install pillow
The output of the model consists of 2 classes for Gender Prediction, namely Male and Female and 9 classes for the Age Prediction, which are (0-2), (3-9), (10-19), (20-29), (30-39), (40-49), (50-59), (60-69), (70+). The table below shows the performance of model when tested on 13835 images containing Indian faces only.
Naming Convention used: True or False represent whether the prediction is Right or Wrong Respectively. Male or Female represents what the model predicted. For eg: False Female means the model predicted Wrong and Prediction was Female. Which means actually it was Male.
| Age Group | True Male | False Male | True Female | False Female |
|---|---|---|---|---|
| (0-2) | 55 | 32 | 88 | 12 |
| (3-9) | 561 | 221 | 793 | 138 |
| (10-19) | 784 | 216 | 645 | 70 |
| (20-29) | 1701 | 222 | 1484 | 53 |
| (30-39) | 1157 | 166 | 1752 | 44 |
| (40-49) | 641 | 105 | 1062 | 26 |
| (50-59) | 382 | 65 | 645 | 22 |
| (60-69) | 190 | 56 | 249 | 6 |
| (70+) | 90 | 28 | 69 | 5 |
The Accuracy achieved in Gender Prediction is 88.2%
Below shown is the Confusion matrix of Age Prediction
| Age Group | (0-2) | (3-9) | (10-19) | (20-29) | (30-39) | (40-49) | (50-59) | (60-69) | (70+) |
|---|---|---|---|---|---|---|---|---|---|
| (0-2) | 175 | 8 | 1 | 3 | 0 | 0 | 0 | 0 | 0 |
| (3-9) | 267 | 1187 | 78 | 106 | 24 | 14 | 36 | 1 | 0 |
| (10-19) | 62 | 418 | 651 | 414 | 64 | 50 | 55 | 1 | 0 |
| (20-29) | 53 | 65 | 93 | 2539 | 338 | 264 | 107 | 1 | 0 |
| (30-39) | 38 | 38 | 42 | 942 | 1081 | 722 | 253 | 3 | 0 |
| (40-49) | 17 | 13 | 8 | 174 | 212 | 941 | 468 | 1 | 0 |
| (50-59) | 20 | 3 | 2 | 69 | 37 | 190 | 787 | 5 | 1 |
| (60-69) | 6 | 2 | 2 | 15 | 10 | 38 | 347 | 70 | 11 |
| (70+) | 12 | 0 | 1 | 4 | 2 | 9 | 110 | 19 | 35 |
The Accuracy achieved in Age Prediction is 53.96%(exact) and 86.72%(1-off).