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Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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A Systematic Comparison of Depth Map Representations for Face Recognition.

Stefano Pini¹, Guido Borghi², Roberto Vezzani^1,3

¹DIEF-Dipartimento di Ingegneria Enzo Ferrari, Università Degli Studi di Modena e Reggio Emilia, 41125 Modena, Italy.

Sensors (Basel, Switzerland)

|February 12, 2021

Summary

This summary is machine-generated.

This study explores deep face recognition using active depth sensors. Normal images and point clouds offer superior generalization across different sensors and conditions compared to other methods.

Keywords:

dataset depth map representations depth maps depth sensors face recognition point cloud surface normal voxel

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Area of Science:

Computer Vision
Biometrics
Machine Learning

Background:

Active depth sensors are increasingly common for face recognition.
Current deep learning models struggle with generalization due to limited, sensor-specific datasets.

Purpose of the Study:

To analyze depth map representations and deep learning models for robust face recognition.
To identify optimal configurations for accuracy and generalization across diverse acquisition settings.

Main Methods:

Compared depth/normal images, voxels, and point clouds.
Evaluated 2D/3D Convolutional Neural Networks and PointNet-based networks.
Conducted extensive intra- and cross-dataset experiments on four public databases.

Main Results:

Normal images and point clouds demonstrated superior performance and generalization.
These methods outperformed other 2D and 3D alternatives.
A new dataset, MultiSFace, was introduced to study depth map quality and distance effects.

Conclusions:

Depth-based face recognition benefits significantly from normal image and point cloud representations.
Further research is needed on depth map quality and acquisition parameters for enhanced robustness.