FACE RECOGNITION USING TRANSFER LEARNING

Original article was published on Deep Learning on Medium

FACE RECOGNITION USING TRANSFER LEARNING

Transfer learning

Transfer learning is a popular method in computer vision because it allows us to build accurate models in a timesaving way. With transfer learning, instead of starting the learning process from scratch, you start from patterns that have been learned when solving a different problem. This way you leverage previous learnings and avoid starting from scratch.In computer vision, transfer learning is usually expressed through the use of pre-trained models. A pre-trained model is a model that was trained on a large benchmark dataset to solve a problem similar to the one that we want to solve. Accordingly, due to the computational cost of training such models, it is common practice to import and use models from published literature (e.g. VGG, Inception, MobileNet).

Here I’ve created two folders: train and validation folder each containing images of one of an actor and one of myself.Also I’ve used Mobile Net CNN architecture for image/face recognition.

MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices with TensorFlow Mobile.

MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.

EPOCHS
CORRECT PREDICTION
CORRECT PREDICTION
WRONG PREDICTION
CORRECT PREDICTION

CODE LINK : https://github.com/LostRishi/faceRecog