scholarly journals New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chentao Zhang ◽  
Habte Tadesse Likassa ◽  
Peidong Liang ◽  
Jielong Guo
Keyword(s):  
Big Data ◽  
Robust Regression ◽  
State Of The Art ◽  
The State ◽  
Affine Transformations ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Alternating Direction ◽  
Art Works ◽  
Landmark Localization

In this paper, we developed a new robust part-based model for facial landmark localization and detection via affine transformation. In contrast to the existing works, the new algorithm incorporates affine transformations with the robust regression to tackle the potential effects of outliers and heavy sparse noises, occlusions and illuminations. As such, the distorted or misaligned objects can be rectified by affine transformations and the patterns of occlusions and outliers can be explicitly separated from the true underlying objects in big data. Moreover, the search of the optimal parameters and affine transformations is cast as a constrained optimization programming. To mitigate the computations, a new set of equations is derived to update the parameters involved and the affine transformations iteratively in a round-robin manner. Our way to update the parameters compared to the state of the art of the works is relatively better, as we employ a fast alternating direction method for multiplier (ADMM) algorithm that solves the parameters separately. Simulations show that the proposed method outperforms the state-of-the-art works on facial landmark localization and detection on the COFW, HELEN, and LFPW datasets.

scholarly journals New Robust Regularized Shrinkage Regression for High-Dimensional Image Recovery and Alignment via Affine Transformation and Tikhonov Regularization

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Habte Tadesse Likassa ◽  
Wen Xian ◽  
Xuan Tang
Keyword(s):  
Tikhonov Regularization ◽  
Affine Transformation ◽  
State Of The Art ◽  
High Dimensional ◽  
Affine Transformations ◽  
Regularization Term ◽  
Alternating Direction ◽  
Art Works ◽  
Programming Techniques ◽  
Public Datasets

In this work, a new robust regularized shrinkage regression method is proposed to recover and align high-dimensional images via affine transformation and Tikhonov regularization. To be more resilient with occlusions and illuminations, outliers, and heavy sparse noises, the new proposed approach incorporates novel ideas affine transformations and Tikhonov regularization into high-dimensional images. The highly corrupted, distorted, or misaligned images can be adjusted through the use of affine transformations and Tikhonov regularization term to ensure a trustful image decomposition. These novel ideas are very essential, especially in pruning out the potential impacts of annoying effects in high-dimensional images. Then, finding optimal variables through a set of affine transformations and Tikhonov regularization term is first casted as mathematical and statistical convex optimization programming techniques. Afterward, a fast alternating direction method for multipliers (ADMM) algorithm is applied, and the new equations are established to update the parameters involved and the affine transformations iteratively in the form of the round-robin manner. Moreover, the convergence of these new updating equations is scrutinized as well, and the proposed method has less time computation as compared to the state-of-the-art works. Conducted simulations have shown that the new robust method surpasses to the baselines for image alignment and recovery relying on some public datasets.

scholarly journals FAN-Face: a Simple Orthogonal Improvement to Deep Face Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 12621-12628 ◽  
Author(s):  
Jing Yang ◽  
Adrian Bulat ◽  
Georgios Tzimiropoulos
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Simple Approach ◽  
Feature Integration ◽  
Prior Work ◽  
Shape Information ◽  
Facial Landmarks ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Landmark Localization

It is known that facial landmarks provide pose, expression and shape information. In addition, when matching, for example, a profile and/or expressive face to a frontal one, knowledge of these landmarks is useful for establishing correspondence which can help improve recognition. However, in prior work on face recognition, facial landmarks are only used for face cropping in order to remove scale, rotation and translation variations. This paper proposes a simple approach to face recognition which gradually integrates features from different layers of a facial landmark localization network into different layers of the recognition network. To this end, we propose an appropriate feature integration layer which makes the features compatible before integration. We show that such a simple approach systematically improves recognition on the most difficult face recognition datasets, setting a new state-of-the-art on IJB-B, IJB-C and MegaFace datasets.

A Fast Implementation of DPM-Based Facial Landmark Localization

2013 ◽  
Vol 462-463 ◽  
pp. 416-420
Author(s):  
Ke Ming Cao ◽  
Yan Long Li ◽  
Jian Sheng Chen ◽  
Chang Yang ◽  
Meng Wang ◽  
...  
Keyword(s):  
Pose Estimation ◽  
State Of The Art ◽  
Structured Model ◽  
Tree Structures ◽  
Original Algorithm ◽  
Algorithm Efficiency ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Speed Up ◽  
Landmark Localization

Face detection, pose estimation and facial landmark localization are three fundamental problems in pattern recognition. These three tasks have high request of algorithm efficiency and accuracy. Zhu and Ramanan proposed a model based on mixture of tree structures to solve the three tasks simultaneously and it obtains state-of-the-art result. However, the efficiency of their algorithm is relatively low. Our improved algorithm combines Viola Jones detector and tree-structured model and achieves a speed-up of tens of times even hundreds of times of original algorithm on ordinary laptop according to images of different sizes.

A Local-Global Aggregate Network for Facial Landmark Localization

2020 ◽  
Vol 2020 (8) ◽  
pp. 185-1-185-6
Author(s):  
Ruiyi Mao ◽  
Qian Lin ◽  
Jan P. Allebach
Keyword(s):  
Prediction Accuracy ◽  
State Of The Art ◽  
Critical Role ◽  
Global Information ◽  
Face Analysis ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Aggregate Network ◽  
In The Wild ◽  
Landmark Localization

Facial landmark localization plays a critical role in many face analysis tasks. In this paper, we present a novel local-global aggregate network (LGA-Net) for robust facial landmark localization of faces in the wild. The network consists of two convolutional neural network levels which aggregate local and global information for better prediction accuracy and robustness. Experimental results show our method overcomes typical problems of cascaded networks and outperforms state-of-the-art methods on the 300-W [1] benchmark.

scholarly journals Facial Expression Detection and Recognition through VIOLA-JONES Algorithm and HCNN using LSTM Method

2021 ◽  
pp. 463-480
Author(s):  
Dinesh Kumar P ◽  
Dr. B. Rosiline Jeetha
Keyword(s):  
Facial Expression ◽  
Expression Recognition ◽  
Scale Invariant ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Invariant Feature ◽  
Scale Invariant Feature ◽  
Localization Approach ◽  
Landmark Localization ◽  
Detection And Recognition

Facial expression, as one of the most significant means for human beings to show their emotions and intensions in the process of communication, plays a significant role in human interfaces. In recent years, facial expression recognition has been under especially intensive investigation, due conceivably to its vital applications in various fields including virtual reality, intelligent tutoring system, health-care and data driven animation. The main target of facial expression recognition is to identify the human emotional state (e.g., anger, contempt, disgust, fear, happiness, sadness, and surprise ) based on the given facial images. This paper deals with the Facial expression detection and recognition through Viola-jones algorithm and HCNN using LSTM method. It improves the hypothesis execution enough and meanwhile inconceivably reduces the computational costs. In feature matching, the author proposes Hybrid Scale-Invariant Feature Transform (SIFT) with double δ-LBP (Dδ-LBP) and it utilizes the fixed facial landmark localization approach and SIFT’s orientation assignment, to obtain the features that are illumination and pose independent. For face detection, basically we utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and it helps to perform the feature selection through the whale optimization algorithm, once after compression and further, it minimizes the feature vector given into the Hybrid Convolutional Neural Network (HCNN) and Long Short-Term Memory (LSTM) model for identifying the facial expression in efficient manner.The experimental result confirms that the HCNN-LSTM Model beats traditional deep-learning and machine-learning techniques with respect to precision, recall, f-measure, and accuracy using CK+ database. Proposes Hybrid Scale-Invariant Feature Transform (SIFT) with double δ-LBP (Dδ-LBP) and it utilizes the fixed facial landmark localization approach and SIFT’s orientation assignment, to obtain the features that are illumination and pose independent. And HCNN and LSTM model for identifying the facial expression.

Explicit Shape Regression With Characteristic Number for Facial Landmark Localization

2018 ◽  
Vol 20 (3) ◽  
pp. 567-579 ◽  
Author(s):  
Xin Fan ◽  
Risheng Liu ◽  
Zhongxuan Luo ◽  
Yuntao Li ◽  
Yuyao Feng
Keyword(s):  
Characteristic Number ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Shape Regression ◽  
Landmark Localization
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