Entropy-weighted feature-fusion method for head-pose estimation
© The Author(s). 2016
Received: 2 March 2016
Accepted: 2 December 2016
Published: 9 December 2016
This paper proposes a novel entropy-weighted Gabor-phase congruency (EWGP) feature descriptor for head-pose estimation on the basis of feature fusion. Gabor features are robust and invariant to differences in orientation and illuminance but are not sufficient to express the amplitude character in images. By contrast, phase congruency (PC) functions work well in amplitude expression. Both illuminance and amplitude vary over distinctive regions. Here, we employ entropy information to evaluate orientation and amplitude to execute feature fusion. More specifically, entropy is used to represent the randomness and content of information. For the first time, we seek to utilize entropy as weight information to fuse the Gabor and phase matrices in every region. The proposed EWGP feature matrix was verified on Pointing’04 and FacePix. The experimental results demonstrate that our method is superior to the state of the art in terms of MSE, MAE, and time cost.
KeywordsEWGP Head-pose estimation Entropy weighted Gabor Phase congruency Feature fusion
Visual focus of attention (VFoA) is emphasized to estimate at what or whom a person is looking and is highly correlated with head-pose estimation . To study head-pose estimation, three-dimensional orientation parameters from human head images are explored. Head poses convey an abundance of information in natural interpersonal communication (NIC) and human-computer interaction (HCI) ; therefore, an increasing number of researchers is seeking more effective and robust methodologies to estimate head pose. Head poses also play a critical role in artificial intelligence (AI) applications and reveal considerable latent significance of personal intent. For example, people nod their heads to represent understanding in conversations and shake their heads to show dissent, confusion, or consideration. Head orientation with a specific finger-pointing direction generally indicates the place that a person wants to go. The combination of head pose and hand gestures is used to assess the target of an individual’s interest . Mutual orientation indicates that people are involved in discussion. If a person shifts the head toward a specific direction, it is highly likely that there is an object of interest in this direction. Therefore, the study of VFoA as an indicator of conversation target in human-computer interaction and facial-expression recognition is increasingly of interest.
Analyzing head poses is a natural capability of humans but is difficult for AI. However, head-pose estimation has been researched for years, and the state of the art in head-pose estimation can contribute greatly to bridging the gap between humans and AI [4, 5]. Head-pose estimation is generally interpreted as the capability to infer orientation relative to the observation camera. For example, head pose is exploited to determine the focus point on the screen based on the gaze direction . The factors influencing the estimation of head pose and their relationships have been introduced in detail, and the crucial significance of head pose was emphasized in . These factors are mostly related to the surroundings, including camera calibration, head features, glasses, hair, beard, illuminance variations, and image transformations.
To address the shortcomings of existing methods, we concentrate on regional feature extraction based on entropy information. We aim to utilize an information entropy model to assess randomness and content as feature metrics for a specific region for the first time. We then employ the more adaptive feature to represent the virtual region. In addition, the normalized entropy information is regarded as a weight metric to fuse the ultimate feature matrix. The experimental results demonstrate that our feature matrix is superior to the state-of-the-art.
This paper is structured as follows: Section 1.2 provides an exhaustive overview of previous related work in head-pose estimation. Section 1.3 presents the proposed methodology step by step, including a skin model for face detection using Gabor features, PC features, and entropy-weighted Gabor phase congruency (EWGP). Section 1.4 describes the experiments using the Pointing’04 dataset. Finally, in Section 2, we present our conclusions and discuss future work.
1.2 Related work
In general, head-pose estimation approaches can be classified into two types: coarse level and fine level . The former commonly employ algorithms to calculate a few discrete head orientations, such as left, right, and looking up. The latter generally utilize methodologies to compute the continuous pose in accurate angles. Here, we redefine the coarse level and fine level: coarse-level approaches recognize the head-orientation variations using discrete estimation and accurate computation, and fine-level approaches indicate the intentions or interests of the experimental subjects. The computational approaches of both layers can be divided into statistical and non-statistical types based on their dependence on statistical methods or not.
1.2.1 Statistical approaches
The most classical statistical method is to exploit classifiers or regression methods to recognize specific discrete head poses. Multi-classification tools such as a support vector machine (SVM) are utilized to estimate discrete head poses. SVM has been employed to locate the iris centers in approximately detected eye regions  and to distinguish frontal and look-up head-pose variations in a Carnegie Mellon University (CMU) face dataset . Support vector regression (SVR) is an alternate version that is used for the continuous problem. The differences in head-pose estimation between SVM and SVR have been described in detail . SVR performs well for either horizontal or vertical head-pose variations, whereas SVM performs better for vertical variations than for horizontal. If the search range is not extensive, the combination of SVM and SVR is a good option. In addition, whenever the number of classes changes, the SVMs must be re-trained from scratch.
Regression is another typical statistical method that is available for both discrete and continuous head-orientation angle estimation. Examples of regression approaches include the aforementioned SVR and multi-layer perceptrons (MLP). Regression approaches are classified as linear and nonlinear based on the causal relationships between independent variables and dependent variables. An MLP can also be trained for fine head-pose estimation over a continuous pose range. In this configuration, the network has one output for each DOF. The activation of the output is proportional to its corresponding orientation [13–15]. The high dimension of an image presents a challenge for some regression tools. More specially, regression methods cannot resolve the need for long, sophisticated training and are highly sharply sensitive to head localization. In summary, dimension reduction via principle component analysis (PCA)  or its nonlinear kernel version (KPCA)  or localized gradient-orientation histograms  is necessary during the above procedure.
Instead of comparing images to a large set of discrete class labels or a series regression values, the probe image can be measured by a detector array that is also trained on many images with supervised learning methods. Detector array methods are well suited for both high- and low-resolution images. In addition, they are superior in sub-regional operations. Most importantly, these methods do not require separate head detection and localization. The drawbacks of these schemes are the necessary scale of training, binary output of detectors, and low accuracy; in practice, a maximum of 12 different detectors can be formed, which limits the pose-estimation definition to less than 12 states .
High-dimensional image samples can lie on a low-dimensional manifold that is constrained to meet the pose variations. Manifold-embedding methodologies, including isometric feature mapping (Isomap) [19, 20], locally linear embedding (LLE) , and Laplacian eigenmaps (LE) , have shown promise for head-pose estimation by mapping high-dimensional data into low-dimensional space. Such low-dimensional spaces can be formed by classification or regression. However, the limitation of typical PCA is not averted for nonlinear head-pose variations. Since unsupervised methods are utilized during the classification or regression, these methods are not available to incorporate the class labels during head-pose training. Most importantly, the aforementioned techniques cannot ensure that each class is expressed as a single label.
1.2.2 Non-statistical approaches
Experimental results have revealed considerable differences between statistical methods and non-statistical measurements [23–26]. The former mainly focus on appearance-based measurements, whereas the latter usually consider geometric relationship cues, such as the deviation of the nose from the mid-line and the deviation between the new head pose and the original state. In non-statistical methods, flexible models, geometric information, and motion trajectory are employed to estimate head pose.
Flexible models seek to fit non-rigid models with facial features and contribute to the exploration of the facial structure in both discrete and continuous head orientations. Among flexible models, active shape models (ASM) [27, 28] and active appearance models (AAM) exhibit higher accuracy and robustness . These approaches permit the direct prediction of head pose when an inherent 3D model constrains the fitting of 2D points. Combination of the 3D model and 2D points enables direct head-pose computation using structure-from-motion algorithms. In summary, flexible models have great potential for both high accuracy and good robustness in head-pose estimation, but these qualities are strictly correlated with the relative extracted feature positions and image definition. Additionally, geometric methods exploit relative feature positions to estimate head pose; however, the accuracy is highly related to the feature-point extraction . Importantly, the highest accuracies of the presented approaches are at least 1–2 pixels. Unfortunately, each pixel error generally relates to an angle error of approximately 5°. Consequently, geometric measurements cannot serve as precise head-pose estimates in cases of limited feature-point detection. The use of motion-trajectory tracking methods between subsequent video frames outperforms the other aforementioned methods [27, 31, 32]. In previous work, we employed a SIFT feature-point and bio compound-eye mechanism to explore object-tracking measurements with superior robustness and accuracy . Tracking methods operate in a bottom-up manner, following low-level facial landmarks from frame to frame. Typically, the subject must maintain a frontal pose before the system started. The track system must be reinitialized whenever the object of interest is lost. As a result, geometrical approaches often rely on manual initialization or a camera view in which the subject’s neutral head pose is forward-looking and easily reinitialized with a frontal face detector . Recently, a number of hybrid approaches have been proposed [33–38] that integrate the remarkable advances in the above statistical and non-statistical methods to provide the best accuracy and robustness in head-pose estimation.
Our proposed method is a hybrid approach, and we seek to estimate head poses on the coarse level to compute the orientation angles using some machine classifiers and geometrical information. Information entropy is a good indicator of information representation with respect to randomness and content. Histogram of gradient (HoG), Gabor, and phase congruency (PC) are effectively and commonly used in direction estimation. However, the dimensionality of these feature matrices is usually too high for image representation. With the development of technology, image-definition has increased abruptly. More specifically, dimension-disaster frequency has clearly risen. This paper presents an entropy-weighted method to fuse Gabor and PC features and exploits entropy as a weight metric to reinforce randomness for the first time. Additionally, entropy plays an important role in dimension reduction and image annotation. The experimental results prove that our solution is effective in reducing the dimension and shows good accuracy and robustness to variations of head pose.
1.3 Proposed methodology
1.3.1 Skin-color model
1.3.2 Gabor features
1.3.3 Phase congruency
A i indicates the amplitude of the ith Fourier component, φ i (x) represents the ith local phase of the components, and φ(x) is the weighted mean of all local phase angles at the objective location. Additionally, for each frequency w i , A i is the amplitude of the cosine wave, and φ i (x)−φ(x) is the phase offset of that wave. The term T is related to the size of the image window, and we will assume it a value of 1. It is important to assume that phase-congruency features differ from one another when dealing with different head-orientation probe images. Consequently, it is necessary to distinguish which filter orientation is more effective in pose estimation. In our case, the Pointing’04 head-pose dataset was utilized to evaluate the phase-congruency features after face detection by the eclipse skin model. To this end, binary-edge images were collected.
1.3.4 EWGP feature fusion
A larger value of D represents a closer relationship with the probe representation. We employ Jeffrey’s entropy as the weight to construct the new feature matrix. Meanwhile, dimension-reduction operations are utilized to optimize the Gabor feature matrix and PC feature matrix, such as PCA and SVD. In summary, in our case, the advantages of Gabor features and PC features are combined for the first time to estimate head pose in Eq. (9), where W is the normalized entropy weight for Gabor and PC in the i×jth sub-region.
1.4 Experiments and analysis of results
Estimation results on Pointing’04
CVA (SVM) %
Comparison of head-pose estimation results on the Point’04 database
In this study, a novel entropy-weighted Gabor and phase-congruency (EWGP) feature matrix was built on the condition of feature fusion. We successfully applied EWGP in multi-classification for head-pose estimation in still imagery and a real-time video stream with homogeneous and heterogeneous data. Our experimental results demonstrated that the proposed EWGP method outperforms state-of-the-art when estimating head pose in terms of MSE, CVA, MAE, and time cost. Unfortunately, head pose only describes the direction in which a person is looking and does not provide information on the object of interest. Therefore, it is necessary to focus on additional information, such as visual saliency in head orientation, gaze direction, and hand gestures. In future works, we plan to expand head-pose estimation to include gaze estimation and obtain a better understanding of the object of an individual’s interest.
Degree of freedom
Entropy-weighted Gabor and phase congruency
Isometric feature mapping
Kernel principle component analysis
Natural interpersonal communication
Principle component analysis
Support vector machine
Support vector regression
Visual focus of attention
Thanks to the partners for providing their test results. Gratitude is also expressed to my mentor Xu Qian for his valuable and helpful suggestions.
XMW proposed the main idea to fuse Gabor and phase-congruency features using information entropy and participated in carrying out the experiments using Pointing’04 datasets to verify the proposed method. KL performed some experiments to verify the relationship between the skin-color model and face extraction and participated in extracting face regions from the Pointing’04 and FacePix datasets. XQ helped with the statistical analysis and drafting of the manuscript. All authors have read and approved the final manuscript.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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