Introduction to DeepFace DeepFace is one of the earliest deep learning-based models designed for facial recognition. Developed by Facebook in 2014, it achieved near-human-level accuracy on facial verification tasks. The core idea behind DeepFace is to map facial images into a high-dimensional feature space, where the distance between two points corresponds to the similarity between the faces.
Key Concepts in DeepFace Face Detection: The first step involves detecting and aligning faces from the input images. This ensures that the faces are centered and normalized, reducing variations due to pose, lighting, and facial expressions.
Face Representation: DeepFace uses a deep neural network to represent each face as a vector of numbers. This vector is a compact summary of the facial features and is often referred to as an embedding.
Similarity Measurement: Once the faces are represented as vectors, the similarity between two faces can be measured by calculating the distance between their corresponding vectors. Typically, Euclidean distance or cosine similarity is used for this purpose.
Face Verification vs. Face Identification:
Verification: Determines if two faces belong to the same person. Identification: Recognizes a face from a set of known faces