Effective face frontalization in unconstrained images

Hassner, Tal; Har-El, Shai; Paz, Eran; Enbar, Roee

doi:10.1109/cvpr.2015.7299058

Cited by 536 publications

(453 citation statements)

References 36 publications

Supporting

Mentioning

447

Contrasting

Unclassified

Order By: Relevance

“…Once we successfully fit our generic 3D face shape model to a given face image, we can render face images with arbitrary yaw-pitch-roll parameters (details can be found in [10] and [7]). For example, Figure 3 shows different face images aligned to the same yaw-pitchroll configuration.…”

Section: Facial Landmark Detection and Face Alignmentmentioning

confidence: 99%

Face recognition using deep multi-pose representations

AbdAlmageed

Rawls

et al. 2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

Self Cite

134

View full text Add to dashboard Cite

We introduce our method and system for face recognition using multiple pose-aware deep learning models. In our representation, a face image is processed by several posespecific deep convolutional neural network (CNN) models to generate multiple pose-specific features. 3D rendering is used to generate multiple face poses from the input image. Sensitivity of the recognition system to pose variations is reduced since we use an ensemble of pose-specific CNN features. The paper presents extensive experimental results on the effect of landmark detection, CNN layer selection and pose model selection on the performance of the recognition pipeline. Our novel representation achieves better results than the state-of-the-art on IARPA's CS2 and NIST's IJB-A in both verification and identification (i.e. search) tasks.

show abstract

Section: Facial Landmark Detection and Face Alignmentmentioning

confidence: 99%

Face recognition using deep multi-pose representations

AbdAlmageed

Rawls

et al. 2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

Self Cite

134

View full text Add to dashboard Cite

show abstract

“…The work (Hassner, Harel, Paz & Enbar, 2015) shows the model that is an array of depth values 181x122 and it is chosen as a unified 3D face model.…”

Section: Methods Of Face Position Correctionmentioning

confidence: 99%

Methods of Automatic Face Angle Recognition for Life Support and Safety Systems

Nebaba¹,

Zakharova²,

Sidorenko³

et al. 2017

The European Proceedings of Social and Behavioural Sciences

View full text Add to dashboard Cite

The vision of the surrounding and people that are within eyeshot influences the human well-being and safety. The rationale of system development that allows recognizing faces from difficult perspectives online and informing timely about approaching people is undisputed. The manuscript describes the methods of automatic detection of equilibrium face points in the bitmap image and methods of forming 3D face model. The optimal search algorithm for equilibrium points has been chosen. The method of forming 3D face model basing on a single bitmap image and building up the face image rotated to the preset angle has been proposed. The algorithm for estimating the angle and algorithm of the face image rotation have been implemented. The manuscript also reviews the existing methods of forming 3D face model. The algorithm for the formation of 3D face model from a single bitmap image and a set of individual 3D models have been proposed as well as the algorithm for forming different face angles with the calculated 3D face model aimed to create biometric vectors cluster. Operation results of the algorithm for face images formation from different angles have been presented.

show abstract

“…Algunos de estos factores que dificultan su proceso, como son las oclusiones que evita un buen reconocimiento, ya sean por gestos que realice las personas, elementos que cubran la cara, iluminación, distancia, envejecimiento, entre otros (Ding, Xu, & Tao, 2015;Hassner, Harel, Paz, & Enbar, 2015;Pizarro Jara, 2011). El problema principal de este proyecto es implementar un sistema de reconocimiento facial, sobre rostros frontales, sobre un dispositivo embebido.…”

Section: Introductionunclassified

RECONOCIMIENTO FACIAL BASADO EN EIGENFACES, LBHP Y FISHERFACES EN LA BEAGLEBOARD-xM

Franco¹,

Ospina²,

Cuevas³

et al. 2017

RCTA

View full text Add to dashboard Cite

El presente trabajo consiste en la implementación de un sistema de detección de rostro aplicando procesamiento de imágenes, aplicando características basados en eigenfaces, Histogramas de patrones locales binarios LBPH y discriminantes Fisherfaciales, sobre el dispositivo embebido BeagleBoard-xM. Para desarrollar este Trabajo se empelaronlibrerías de OpenCV, Java, CMake y sistemas operativos como Debian y Ubuntusobre la BeagleBoard. La validación del sistema determinó que el mejor resultado se obtiene con la técnica de EigenFaces ya que presento menor error de clasificación y 0% de errores de falsos positivos.

show abstract

Effective face frontalization in unconstrained images

Cited by 536 publications

References 36 publications

Face recognition using deep multi-pose representations

Face recognition using deep multi-pose representations

Methods of Automatic Face Angle Recognition for Life Support and Safety Systems

RECONOCIMIENTO FACIAL BASADO EN EIGENFACES, LBHP Y FISHERFACES EN LA BEAGLEBOARD-xM

Contact Info

Product

Resources

About