scholarly journals Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment

2014 ◽  
pp. 1-16 ◽  
Author(s):  
Jie Zhang ◽  
Shiguang Shan ◽  
Meina Kan ◽  
Xilin Chen
Keyword(s):  
Real Time ◽  
Face Alignment ◽  
Coarse To Fine

Robust face alignment with cascaded coarse-to-fine auto-encoder network

2017 ◽  
Author(s):  
Cheng Peng ◽  
Yongxin Ge ◽  
Mingjian Hong ◽  
Sheng Huang ◽  
Dan Yang
Keyword(s):  
Face Alignment ◽  
Coarse To Fine

scholarly journals Face Alignment by Coarse-to-Fine Shape Estimation

2018 ◽  
Vol 27 (6) ◽  
pp. 1183-1191 ◽  
Author(s):  
Jun Wan ◽  
Jing Li ◽  
Jun Chang ◽  
Yujia WU ◽  
Yafu Xiao ◽  
...  
Keyword(s):  
Face Alignment ◽  
Shape Estimation ◽  
Coarse To Fine

scholarly journals Real-time facial pose estimation and tracking by coarse-to-fine iterative optimization

2020 ◽  
Vol 25 (5) ◽  
pp. 690-700
Author(s):  
Xiaolong Yang ◽  
Xiaohong Jia ◽  
Mengke Yuan ◽  
Dong-Ming Yan
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Iterative Optimization ◽  
Coarse To Fine

scholarly journals Fast Finger Vein Recognition Based on Sparse Matching Algorithm under a Multicore Platform for Real-Time Individuals Identification

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1167
Author(s):  
Ruber Hernández-García ◽  
Ricardo J. Barrientos ◽  
Cristofher Rojas ◽  
Wladimir E. Soto-Silva ◽  
Marco Mora ◽  
...  
Keyword(s):  
Real Time ◽  
State Of The Art ◽  
Individual Identification ◽  
Affine Transformations ◽  
Private Companies ◽  
Time Efficiency ◽  
Finger Vein ◽  
Vein Recognition ◽  
Coarse To Fine ◽  
Finger Vein Recognition

Nowadays, individual identification is a problem in many private companies, but also in governmental and public order entities. Currently, there are multiple biometric methods, each with different advantages. Finger vein recognition is a modern biometric technique, which has several advantages, especially in terms of security and accuracy. However, image deformations and time efficiency are two of the major limitations of state-of-the-art contributions. In spite of affine transformations produced during the acquisition process, the geometric structure of finger vein images remains invariant. This consideration of the symmetry phenomena presented in finger vein images is exploited in the present work. We combine an image enhancement procedure, the DAISY descriptor, and an optimized Coarse-to-fine PatchMatch (CPM) algorithm under a multicore parallel platform, to develop a fast finger vein recognition method for real-time individuals identification. Our proposal provides an effective and efficient technique to obtain the displacement between finger vein images and considering it as discriminatory information. Experimental results on two well-known databases, PolyU and SDUMLA, show that our proposed approach achieves results comparable to deformation-based techniques of the state-of-the-art, finding statistical differences respect to non-deformation-based approaches. Moreover, our method highly outperforms the baseline method in time efficiency.

SVM-BASED PHONEME CLASSIFICATION AND LIP SHAPE REFINEMENT IN REAL-TIME LIP-SYNCH SYSTEM

2006 ◽  
Vol 20 (07) ◽  
pp. 1029-1051 ◽  
Author(s):  
HANSEOK KO ◽  
DAVID K. HAN
Keyword(s):  
Real Time ◽  
Processing Time ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Phoneme Classification ◽  
Second Stage ◽  
Single Frame ◽  
Time Operation ◽  
Real Time Operation ◽  
Coarse To Fine

In this paper, we present a real time lip-synch system that activates 2-D avatar's lip motion in synch with incoming speech utterance. To achieve the real time operation of the system, the processing time was minimized by "merge and split" procedures resulting in coarse-to-fine phoneme classification. At each stage of phoneme classification, the support vector machine (SVM) method was applied to reduce the computational load while maintaining the desired accuracy. The coarse-to-fine phoneme classification, is accomplished via two_stages of feature extraction: in the first stage, each speech frame is acoustically analyzed for three classes of lip opening using Mel Frequency Cepstral Coefficients (MFCC) as a feature; in the second stage, each frame is further refined for detailed lip shape using formant information. The method was implemented in 2-D lip animation and it was demonstrated that the system was effective in accomplishing real-time lip-synch. This approach was tested on a PC using the Microsoft Visual Studio with an Intel Pentium IV 1.4 Giga Hz CPU and 384 MB RAM. It was observed that the methods of phoneme merging and SVM achieved about twice the speed in recognition than the method employing the Hidden Markov Model (HMM). A typical latency time per a single frame observed using the proposed method was in the order of 18.22 milliseconds while an HMM method under identical conditions resulted about 30.67 milliseconds.

scholarly journals Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

2019 ◽  
Vol 9 (20) ◽  
pp. 4344 ◽  
Author(s):  
Yang ◽  
Li ◽  
Min ◽  
Wang
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Region Of Interest ◽  
Detection Methods ◽  
Time Performance ◽  
Face Area ◽  
The Face ◽  
Two Stages ◽  
Coarse To Fine ◽  
Identification Mechanism

Although the face detection problem has been studied for decades, searching tiny faces in the whole image is still a challenging task, especially in low-resolution images. Traditional face detection methods are based on hand-crafted features, but the features of tiny faces are different from those of normal-sized faces, and thus the detection robustness cannot be guaranteed. In order to alleviate the problem in existing methods, we propose a pre-identification mechanism and a cascaded detector (PMCD) for tiny-face detection. This pre-identification mechanism can greatly reduce background and other irrelevant information. The cascade detector is designed with two stages of deep convolutional neural network (CNN) to detect tiny faces in a coarse-to-fine manner, i.e., the face-area candidates are pre-identified as region of interest (RoI) based on a real-time pedestrian detector and the pre-identification mechanism, the set of RoI candidates is the input of the second sub-network instead of the whole image. Benefiting from the above mechanism, the second sub-network is designed as a shallow network which can keep high accuracy and real-time performance. The accuracy of PMCD is at least 4% higher than the other state-of-the-art methods on detecting tiny faces, while keeping real-time performance.

Sign in / Sign up

Export Citation Format

Share Document