Building a Face Recognition App with FaceNet: From Image Preprocessing to Similarity Comparison
In this article we are want to use AI to make a face recognition application using the FaceNet Model as the core of our implementation.
FaceNet is a deep learning model that maps facial images to a compact Euclidean space where distances directly correspond to a measure of face similarity. In simpler terms, it converts a face image into a list of 128 numbers (an embedding) that uniquely represents that face.
Before we can use FaceNet, we need to preprocess our images which includes
Reading the image from the file, Converting it from BGR to RGB color space, Resizing it to 160x160 pixels (the input size expected by FaceNet) and Normalizing the pixel values. Once we have preprocessed the image in question, we can generate its face embedding. now as for the main process of our application we will pass two images two compare if they include the same person therefore we need to preprocess both images, Generate face embeddings for them, Calculate the cosine similarity between the two embeddings and at last compare the similarity to a threshold to determine if the faces match.
Similarity score: 0.33. The images are likely of different people.
Conclusion
In conclusion, Image preprocessing, Feature extraction (via face embeddings) and similarity comparison are the three main steps of face recognition. While this implementation is straightforward, it's important to note that it assumes the input images are clear, front-facing portraits. For more robust real-world applications, additional steps like face detection and alignment would be necessary.