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EBGM is a facial recognition technique that uses a graph-based representation of facial features and compares the faces by measuring the similarity of their graphs. It's used for matching faces across different expressions and aging effects.

CNNs are a class of deep neural networks, often composed of multiple convolution layers with pooling and fully connected layers. They automatically learn hierarchical feature representations from facial images. CNNs are widely used for face detection, recognition, and automated image tagging.

3D Face Reconstruction algorithms create a 3D model of a face from 2D images, using techniques like stereo photogrammetry, or shape from shading. They're used in virtual reality, gaming, and facial surgery planning.

Gabor Wavelets are used in image processing for texture representation and discrimination, which includes local spatial frequency analysis, orientation selection, and scaling. They are used in biometric identification and expression recognition.

Fisherfaces is an algorithm that uses Linear Discriminant Analysis (LDA) to find a linear combination of features that separates face images into classes. It's particularly useful in scenarios where the variance between classes is emphasized. Applications include gender classification and face recognition in challenging lighting conditions.

FaceNet is a deep convolutional network that learns a mapping of face images to a compact Euclidean space where distances directly correspond to face similarity. Applications include face verification and clustering.

LBPH is a texture descriptor used for face recognition that involves comparing the pixel values with their neighboring pixels, encoding these relationships into a histogram. It's robust to illumination changes and is used in various real-world security systems.

SVMs are supervised learning models that analyze data and recognize patterns. They're used for classification and regression challenges, including face recognition by finding the optimal hyperplane that best separates data classes.

DeepFace employs a deep learning model with a very large dataset to achieve near-human level performance in face verification. It uses a nine-layer neural network to learn rich face features. Applications include user authentication and photo tagging on social networks.