Fourier Locally Linear Soft Constrained MACE for facial landmark localization

This paper proposes a novel nonlinear correlation filter for facial landmark localization. Firstly, we prove that SVM as a classifier can also be used for localization. Then, soft constrained Minimum Average Correlation Energy filter （soft constrained MACE） is proposed, which is more resistent to overfittings to training set than other variants of correlation filter. In order to improve the performance for the multi-mode of the targets, locally linear framework is introduced to our model, which results in Fourier Locally Linear Soft Constraint MACE （FL^2 SC-MACE）. Furthermore, we formulate the fast implementation and show that the time consumption in test process is independent of the number of training samples. The merits of our method include accurate localization performance, desiring generalization capability to the variance of objects, fast testing speed and insensitivity to parameter settings. We conduct the cross-set eye localization experiments on challenging FRGC, FERET and BioID datasets. Our method surpasses the state-of-arts especially in pixelwise accuracy.

Estimating Anthropometric Soft Biometrics:An Empirical Method

Following the success of soft biometrics over traditional biomet-rics,anthropometric soft biometrics are emerging as candidate features for recognition or retrieval using an image/video.Anthropometric soft biometrics uses a quantitative mode of annotation which is a relatively better method for annotation than qualitative annotations adopted by traditional biometrics.However,one of the most challenging tasks is to achieve a higher level of accuracy while estimating anthropometric soft biometrics using an image or video.The level of accuracy is usually affected by several contextual factors such as overlapping body components,an angle from the camera,and ambient conditions.Exploring and developing such a collection of anthropometric soft biometrics that are less sensitive to contextual factors and are relatively easy to estimate using an image or video is a potential research domain and it has a lot of value for improved recognition or retrieval.For this purpose,anthro-pometric soft biometrics,which are originally geometric measurements of the human body,can be computed with ease and higher accuracy using landmarks information from the human body.To this end,several key contributions are made in this paper;i)summarizing a range of human body pose estimation tools used to localize dozens of different multi-modality landmarks from the human body,ii)a critical evaluation of the usefulness of anthropometric soft biometrics in recognition or retrieval tasks using state of the art in the field,iii)an investigation on several benchmark human body anthropometric datasets and their usefulness for the evaluation of any anthropometric soft biometric system,and iv)finally,a novel bag of anthropometric soft biomet-rics containing a list of anthropometrics is presented those are practically possible to measure from an image or video.To the best of our knowledge,anthropometric soft biometrics are potential features for improved seamless recognition or retrieval in both constrained and unconstrained scenarios and they also minimize the approximation level of feature value estimation than traditional biometrics.In our opinion,anthropometric soft biometrics constitutes a practical approach for recognition using closed-circuit television(CCTV)or retrieval from the image dataset,while the bag of anthropometric soft biometrics presented contains a potential collection of biometric features which are less sensitive to contextual factors.

ARCosmetics:a real-time augmented reality cosmetics try-on system

A virtual cosmetics try-on system provides a realistic try-on experience for consumers and helps them efficiently choose suitable cosmetics.In this article,we propose a real-time augmented reality virtual cosmetics try-on system for smartphones(ARCosmetics),taking speed,accuracy,and stability into consideration at each step to ensure a better user experience.A novel and very fast face tracking method utilizes the face detection box and the average position of facial landmarks to estimate the faces in continuous frames.A dynamic weight Wing loss is introduced to assign a dynamic weight to every landmark by the estimated error during training.It balances the attention between small,medium,and large range error and thus increases the accuracy and robustness.We also designed a weighted average method to utilize the information of the adjacent frame for landmark refinement,guaranteeing the stability of the generated landmarks.Extensive experiments conducted on a large 106-point facial landmark dataset and the 300-VW dataset demonstrate the superior performance of the proposed method compared to other state-of-the-art methods.We also conducted user satisfaction studies further to verify the efficiency and effectiveness of our ARCosmetics system.

Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics

As a typical biometric cue with great diversities, smile is a fairly influential signal in social interaction, which reveals the emotional feeling and inner state of a person. Spontaneous and posed smiles initiated by different brain systems have differences in both morphology and dynamics. Distinguishing the two types of smiles remains challenging as discriminative subtle changes need to be captured, which are also uneasily observed by human eyes. Most previous related works about spontaneous versus posed smile recognition concentrate on extracting geometric features while appearance features are not fully used, leading to the loss of texture information. In this paper, we propose a region-specific texture descriptor to represent local pattern changes of different facial regions and compensate for limitations of geometric features. The temporal phase of each facial region is divided by calculating the intensity of the corresponding facial region rather than the intensity of only the mouth region. A mid-level fusion strategy of support vector machine is employed to combine the two feature types. Experimental results show that both our proposed appearance representation and its combination with geometry-based facial dynamics achieve favorable performances on four baseline databases： BBC, SPOS, MMI, and UvA-NEMO.