Supervised descent method (SDM) applied to accurate pupil detection in off-the-shelf eye tracking systems

The precise detection of pupil/iris center is key to estimate gaze accurately. This fact becomes specially challenging in low cost frameworks in which the algorithms employed for high performance systems fail. In the last years an outstanding effort has been made in order to apply training-based methods to low resolution images. In this paper, Supervised Descent Method (SDM) is applied to GI4E database. The 2D landmarks employed for training are the corners of the eyes and the pupil centers. In order to validate the algorithm proposed, a cross validation procedure is performed. The strategy employed for the training allows us to affirm that our method can potentially outperform the state of the art algorithms applied to the same dataset in terms of 2D accuracy. The promising results encourage to carry on in the study of training-based methods for eye tracking.Spanish Ministry of Economy,Industry and Competitiveness, contracts TIN2014-52897-R and TIN2017-84388-

SeTA: semiautomatic tool for annotation of eye tracking images

Availability of large scale tagged datasets is a must in the field of deep learning applied to the eye tracking challenge. In this paper, the potential of Supervised-Descent-Method (SDM) as a semiautomatic labelling tool for eye tracking images is shown. The objective of the paper is to evidence how the human effort needed for manually labelling large eye tracking datasets can be radically reduced by the use of cascaded regressors. Different applications are provided in the fields of high and low resolution systems. An iris/pupil center labelling is shown as example for low resolution images while a pupil contour points detection is demonstrated in high resolution. In both cases manual annotation requirements are drastically reduced.Spanish Ministry of Science, Innovation and Universities, contract TIN2017-84388-

Facial landmark localization in depth images using supervised ridge descent

Berk Gökberk (MEF Author)Supervised Descent Method (SDM) has proven successful in many computer vision applications such as face alignment, tracking and camera calibration. Recent studies which used SDM, achieved state of the-art performance on facial landmark localization in depth images [4]. In this study, we propose to use ridge regression instead of least squares regression for learning the SDM, and to change feature sizes in each iteration, effectively turning the landmark search into a coarse to fine process. We apply the proposed method to facial landmark localization on the Bosphorus 3D Face Database; using frontal depth images with no occlusion. Experimental results confirm that both ridge regression and using adaptive feature sizes improve the localization accuracy considerably.WOS:000380434700048Scopus - Affiliation ID: 60105072Conference Proceedings Citation Index- ScienceProceedings PaperAralık2015YÖK - 2015-1

Face Alignment using Modified Supervised Descent Method

Face alignment has been used on preprocess stage in computer vision’s problems. One of the best methods for face aligment is Supervised Descent Method (SDM). This method seeks the weight of non-linear features which is used for making the product and the feature resulting estimation on the changes of optimal distance of early landmark point towards the actual location of the landmark points (GTS). This article presented modifications of the SDM on the generation of some early forms as a sample on the training stage and an early form on the test stage. In addition, the pyramid image was used as the image for feature extraction process used in the training phase on linear regression. 1€ filter was used to stabilize the movement of estimated landmark points. It was found that the accuracy of the method in BioID dataset with 1000 training images in RMSE is approximately 0.882

The Conditional Lucas & Kanade Algorithm

The Lucas & Kanade (LK) algorithm is the method of choice for efficient dense image and object alignment. The approach is efficient as it attempts to model the connection between appearance and geometric displacement through a linear relationship that assumes independence across pixel coordinates. A drawback of the approach, however, is its generative nature. Specifically, its performance is tightly coupled with how well the linear model can synthesize appearance from geometric displacement, even though the alignment task itself is associated with the inverse problem. In this paper, we present a new approach, referred to as the Conditional LK algorithm, which: (i) directly learns linear models that predict geometric displacement as a function of appearance, and (ii) employs a novel strategy for ensuring that the generative pixel independence assumption can still be taken advantage of. We demonstrate that our approach exhibits superior performance to classical generative forms of the LK algorithm. Furthermore, we demonstrate its comparable performance to state-of-the-art methods such as the Supervised Descent Method with substantially less training examples, as well as the unique ability to "swap" geometric warp functions without having to retrain from scratch. Finally, from a theoretical perspective, our approach hints at possible redundancies that exist in current state-of-the-art methods for alignment that could be leveraged in vision systems of the future.Comment: 17 pages, 11 figure

Visual Tracking by Sampling in Part Space

In this paper, we present a novel part-based visual tracking method from the perspective of probability sampling. Specifically, we represent the target by a part space with two online learned probabilities to capture the structure of the target. The proposal distribution memorizes the historical performance of different parts, and it is used for the first round of part selection. The acceptance probability validates the specific tracking stability of each part in a frame, and it determines whether to accept its vote or to reject it. By doing this, we transform the complex online part selection problem into a probability learning one, which is easier to tackle. The observation model of each part is constructed by an improved supervised descent method and is learned in an incremental manner. Experimental results on two benchmarks demonstrate the competitive performance of our tracker against state-of-the-art methods

Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection

Cascade regression framework has been shown to be effective for facial landmark detection. It starts from an initial face shape and gradually predicts the face shape update from the local appearance features to generate the facial landmark locations in the next iteration until convergence. In this paper, we improve upon the cascade regression framework and propose the Constrained Joint Cascade Regression Framework (CJCRF) for simultaneous facial action unit recognition and facial landmark detection, which are two related face analysis tasks, but are seldomly exploited together. In particular, we first learn the relationships among facial action units and face shapes as a constraint. Then, in the proposed constrained joint cascade regression framework, with the help from the constraint, we iteratively update the facial landmark locations and the action unit activation probabilities until convergence. Experimental results demonstrate that the intertwined relationships of facial action units and face shapes boost the performances of both facial action unit recognition and facial landmark detection. The experimental results also demonstrate the effectiveness of the proposed method comparing to the state-of-the-art works.Comment: International Conference on Computer Vision and Pattern Recognition, 201

PENGEMBANGAN APLIKASI PENYELARASAN LANDMARK WAJAH PADA WAJAH DALAM VIDEO DENGAN SUPERVISED DESCENT METHOD TERBANTU 1 € (ONE EURO) FILTER

Penyelarasan landmark wajah atau lebih dikenal dengan istilah face alignment merupakantahap pra proses pada berbagai masalah komputer visi berkaitan dengan wajah. Pada videodengan tingkat noise cukup tinggi dan pergerakan wajah sangat dinamis, pergerakan titik landmark hasil face alignment antar frame cenderung tidak stabil, halus dan natural. Penggunaan beberapa metode filter pada pergerakan titik landmark hasil face alignmentantar frame memberikan efek jitter atau gerakan tak beraturan yang sering terjadi padagerakan wajah lambat dan efek lag atau keterlambatan mengikuti gerakan sebenarnya yang sering terjadi pada gerakan wajah cepat. 1 € (One Euro) Filter merupakan filter berbasisevent yang mampu mengadaptasikan proses filter berdasarkan event. Pemanfaatan 1 € Filter untuk beradaptasi pada event pergerakan wajah cocok digunakan sebagai filter efek lag dan jitter tersebut. Hasil beberapa pengujian pada parameter pelatihan menunjukanbahwa jumlah cluster untuk inisialisasi bentuk awal wajah memberikan hasil lebih baikpada nilai dua kali atau tiga kali nilai parameter jumlah sampel dibanding jumlah clustersama dengan jumlah sampel. Pada parameter ukuran patch gambar dengan nilai 32menghasilkan estimasi landmark wajah lebih akurat dibanding dengan ukuran patchbernilai 16 dan 24. Penggunaan parameter jumlah channel HOG bernilai 9, 13, 17 dan 21tidak meberikan perbedaan akurasi face alignment secara signifikan