# Discriminant Patch Representation for RGB-D Face Recognition Using Convolutional Neural Networks

## TL;DR

This paper introduces a CNN-based method for learning discriminant face patch features from RGB-D data, improving face recognition performance over traditional hand-crafted features.

## Contribution

It presents a novel training approach for CNNs to automatically learn discriminant face features from RGB-D data, enhancing recognition accuracy.

## Key findings

- Achieved competitive results on RGB-D face datasets
- Demonstrated the effectiveness of learned features over hand-crafted ones
- Validated on state-of-the-art datasets with promising performance

## Abstract

This paper focuses on designing data-driven models to learn a discriminant representation space for face recognition using RGB-D data. Unlike hand-crafted representations, learned models can extract and organize the discriminant information from the data, and can automatically adapt to build new compute vision applications faster. We proposed an effective way to train Convolutional Neural Networks to learn face patch discriminant features. The proposed solution was tested and validated on state-of-the-art RGB-D datasets and showed competitive and promising results relatively to standard hand-crafted feature extractors.