52 research outputs found
3D Face Recognition Benchmarks on the Bosphorus Database with Focus on Facial Expressions
This paper presents an evaluation of several 3D face recognizers on the Bosphorus database, which was gathered for studies on expression and pose invariant face analysis. We provide identification results of three 3D face recognition algorithms, namely generic face template based ICP approach, one-to-all ICP approach, and depth image-based Principal Component Analysis (PCA) method. All of these techniques treat faces globally and are usually accepted as baseline approaches. In addition, 2D texture classifiers are also incorporated in a fusion setting. Experimental results reveal that even though global shape classifiers achieve almost perfect identification in neutral-to-neutral comparisons, they are sub-optimal under extreme expression variations. We show that it is possible to boost the identification accuracy by focusing on the rigid facial regions and by fusing complementary information coming from shape and texture modalities
Learning from Millions of 3D Scans for Large-scale 3D Face Recognition
Deep networks trained on millions of facial images are believed to be closely
approaching human-level performance in face recognition. However, open world
face recognition still remains a challenge. Although, 3D face recognition has
an inherent edge over its 2D counterpart, it has not benefited from the recent
developments in deep learning due to the unavailability of large training as
well as large test datasets. Recognition accuracies have already saturated on
existing 3D face datasets due to their small gallery sizes. Unlike 2D
photographs, 3D facial scans cannot be sourced from the web causing a
bottleneck in the development of deep 3D face recognition networks and
datasets. In this backdrop, we propose a method for generating a large corpus
of labeled 3D face identities and their multiple instances for training and a
protocol for merging the most challenging existing 3D datasets for testing. We
also propose the first deep CNN model designed specifically for 3D face
recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our
test dataset comprises 1,853 identities with a single 3D scan in the gallery
and another 31K scans as probes, which is several orders of magnitude larger
than existing ones. Without fine tuning on this dataset, our network already
outperforms state of the art face recognition by over 10%. We fine tune our
network on the gallery set to perform end-to-end large scale 3D face
recognition which further improves accuracy. Finally, we show the efficacy of
our method for the open world face recognition problem.Comment: 11 page
Bosphorus database for 3d face analysis
Abstract. A new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions is presented in this paper. This database is unique from three aspects: i) the facial expressions are composed of judiciously selected subset of Action Units as well as the six basic emotions, and many actors/actresses are incorporated to obtain more realistic expression data; ii) a rich set of head pose variations are available; and iii) different types of face occlusions are included. Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis. 1
FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation
Facial expression analysis based on machine learning requires large number of
well-annotated data to reflect different changes in facial motion. Publicly
available datasets truly help to accelerate research in this area by providing
a benchmark resource, but all of these datasets, to the best of our knowledge,
are limited to rough annotations for action units, including only their
absence, presence, or a five-level intensity according to the Facial Action
Coding System. To meet the need for videos labeled in great detail, we present
a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D
Facial Animation. One hundred and twenty-two participants, including children,
young adults and elderly people, were recorded in real-world conditions. In
addition, 99,356 frames were manually labeled using Expression Quantitative
Tool developed by us to quantify 9 symmetrical FACS action units, 10
asymmetrical (unilateral) FACS action units, 2 symmetrical FACS action
descriptors and 2 asymmetrical FACS action descriptors, and each action unit or
action descriptor is well-annotated with a floating point number between 0 and
1. To provide a baseline for use in future research, a benchmark for the
regression of action unit values based on Convolutional Neural Networks are
presented. We also demonstrate the potential of our FEAFA dataset for 3D facial
animation. Almost all state-of-the-art algorithms for facial animation are
achieved based on 3D face reconstruction. We hence propose a novel method that
drives virtual characters only based on action unit value regression of the 2D
video frames of source actors.Comment: 9 pages, 7 figure
Multimodal database of emotional speech, video and gestures
People express emotions through different modalities. Integration of verbal and non-verbal communication channels creates a system in which the message is easier to understand. Expanding the focus
to several expression forms can facilitate research on emotion recognition as well as human-machine interaction. In this article, the authors
present a Polish emotional database composed of three modalities: facial expressions, body movement and gestures, and speech. The corpora
contains recordings registered in studio conditions, acted out by 16 professional actors (8 male and 8 female). The data is labeled with six basic
emotions categories, according to Ekman’s emotion categories. To check
the quality of performance, all recordings are evaluated by experts and
volunteers. The database is available to academic community and might
be useful in the study on audio-visual emotion recognition
RGBD Datasets: Past, Present and Future
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style
Facial feature point fitting with combined color and depth information for interactive displays
Interactive displays are driven by natural interaction with the user, necessitating a computer system that recognizes body gestures and facial expressions. User inputs are not easily or reliably recognized for a satisfying user experience, as the complexities of human communication are difficult to interpret in real-time. Recognizing facial expressions in particular is a problem that requires high-accuracy and efficiency for stable interaction environments. The recent availability of the Kinect, a low cost, low resolution sensor that supplies simultaneous color and depth images, provides a breakthrough opportunity to enhance the interactive capabilities of displays and overall user experience. This new RGBD (RGB + depth) sensor generates an additional channel of depth information that can be used to improve the performance of existing state of the art technology and develop new techniques. The Active Shape Model (ASM) is a well-known deformable model that has been extensively studied for facial feature point placement. Previous shape model techniques have applied 3D reconstruction techniques using multiple cameras or other statistical methods for producing 3D information from 2D color images. These methods showed improved results compared to using only color data, but required an additional deformable model or expensive imaging equipment. In this thesis, an ASM model is trained using the RGBD image produced by the Kinect. The real-time information from the depth sensor is registered to the color image to create a pixel-for-pixel match. To improve the quality of the depth image, a temporal median filter is applied to reduce random noise produced by the sensor. The resulting combined model is designed to produce more robust fitting of facial feature points compared to a purely color based active shape model
- …