Solve the problem of human recognition from their faces using machine learning techniques.

Objective: Solve the problem of human recognition from their faces using machine learning techniques.

Theory:
Let us introduce a new benchmark data set of face images with variable makeup, hairstyles and occlusions, named BookClub artistic makeup data, and then examine the performance of the ANNs under different conditions. Makeup and other occlusions can be used not only to disguise a person's identity from the ANN algorithms, but also to spoof a wrong identification.

ANN Algorithm:
Artificial Neural Network (ANN) are capable of learning patterns of interest from data in the presence of variations. An Artificial Neural Network in the field of Artificial intelligence where it attempts to mimic the network of neurons makes up a human brain so that computers will have an option to understand things and make decisions in a human-like manner. The artificial neural network is designed by programming computers to behave simply like interconnected brain cells.

Artificial Neural Network primarily consists of three layers:

• Input Layer
• Hidden Layer
• Output Layer

Procedure:

1. The images used in this are kept coloured and downsized and compressed into JPEG format with the dimension of 48x48 pixels.
2. The downsizing is done due to computational restrictions to keep processing times reasonable. However, observations made on the small size images are extendable to larger sizes.
3. For computational experiments, ‘Keras’ library with Tensorflow back-end were used.
4. The ANN consists of the four sequential groups of layers of the Gaussian noise, convolution with ReLU activation functions, normalization, pooling and dropout layers.
5. It is topped with the fully connected layers, the softmax activation function of the last layer and cross-entropy loss function. "Adam" learning algorithm with 0:001 coecient, mini-batch size 32 and 100 epochs parameters are used.

Output:

Conclusion:
Despite the small size images were scaled to and not very deep ANN, mean accuracy of the face recognition of the model trained on the samples from all photo-sessions of all subjects is quite high at 92%, and higher (up to 99:9%)