Robust multi-clue face tracking system

In this paper we present a multi-clue face tracking system, based on the combination of a face detector and two independent trackers. The detector, a variant of the Viola-Jones algorithm, is set to generate very low false positive error rate. It initiates the tracking system and updates its state. The trackers, based on 3DRS and optical flow respectively, have been chosen to complement each other in different conditions. The main focus of this work is the integration of the two trackers and the design of a closed loop detector-tracker system, aiming at achieving superior robustness at real-time operation on a PC platform. Tests were carried out to assess the actual performance of the system. With an average of about 95% correct face location rate and no significant false positives, the proposed approach appears to be particularly robust to complex backgrounds, ambient light variation, face orientation and scale changes, partial occlusions, different\ud facial expressions and presence of other unwanted faces

Face Detection with Effective Feature Extraction

There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple features other than Haar-like features can also be applied for training an effective face detector. Since, single feature is not discriminative enough to separate faces from difficult non-faces, we further improve the generalization performance of our simple features by introducing feature co-occurrences. We demonstrate that our proposed features yield a performance improvement compared to Haar-like features. In addition, our findings indicate that features play a crucial role in the ability of the system to generalize.Comment: 7 pages. Conference version published in Asian Conf. Comp. Vision 201

S3^3FD: Single Shot Scale-invariant Face Detector

This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchor-based detectors deteriorate dramatically as the objects become smaller. We make contributions in the following three aspects: 1) proposing a scale-equitable face detection framework to handle different scales of faces well. We tile anchors on a wide range of layers to ensure that all scales of faces have enough features for detection. Besides, we design anchor scales based on the effective receptive field and a proposed equal proportion interval principle; 2) improving the recall rate of small faces by a scale compensation anchor matching strategy; 3) reducing the false positive rate of small faces via a max-out background label. As a consequence, our method achieves state-of-the-art detection performance on all the common face detection benchmarks, including the AFW, PASCAL face, FDDB and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images.Comment: Accepted by ICCV 2017 + its supplementary materials; Updated the latest results on WIDER FAC

From Categories to Individuals in Real Time — A Unified Boosting Approach

A method for online, real-time learning of individual-object detectors is presented. Starting with a pre-trained boosted category detector, an individual-object detector is trained with near-zero computational cost. The individual detector is obtained by using the same feature cascade as the category detector along with elementary manipulations of the thresholds of the weak classifiers. This is ideal for online operation on a video stream or for interactive learning. Applications addressed by this technique are reidentification and individual tracking. Experiments on four challenging pedestrian and face datasets indicate that it is indeed possible to learn identity classifiers in real-time; besides being faster-trained, our classifier has better detection rates than previous methods on two of the datasets

Asymmetric Pruning for Learning Cascade Detectors

Cascade classifiers are one of the most important contributions to real-time object detection. Nonetheless, there are many challenging problems arising in training cascade detectors. One common issue is that the node classifier is trained with a symmetric classifier. Having a low misclassification error rate does not guarantee an optimal node learning goal in cascade classifiers, i.e., an extremely high detection rate with a moderate false positive rate. In this work, we present a new approach to train an effective node classifier in a cascade detector. The algorithm is based on two key observations: 1) Redundant weak classifiers can be safely discarded; 2) The final detector should satisfy the asymmetric learning objective of the cascade architecture. To achieve this, we separate the classifier training into two steps: finding a pool of discriminative weak classifiers/features and training the final classifier by pruning weak classifiers which contribute little to the asymmetric learning criterion (asymmetric classifier construction). Our model reduction approach helps accelerate the learning time while achieving the pre-determined learning objective. Experimental results on both face and car data sets verify the effectiveness of the proposed algorithm. On the FDDB face data sets, our approach achieves the state-of-the-art performance, which demonstrates the advantage of our approach.Comment: 14 page

Desarrollo de un sistema de reconocimiento de emociones faciales en tiempo real

Una organización del trastorno nutricional ha puesto en práctica un nuevo tratamiento para sus pacientes. Éste está directamente relacionado con la emoción del paciente, cuya emoción se clasifica oralmente. Llegados a ese punto aparece el reto de automatizar esta tarea, desarrollando un sistema de reconocimiento de emociones faciales en tiempo real caracterizado por un detector de rostros, un detector de Key Points y una red neuronal convolucional utilizando "DeepLearning" para extraer las características del rostro detectado.An nutritional disorder organization has implemented a new treatment for its patients. This is directly related to the mood of the patient, whose emotion is classifiel orally. At this point, the challenge is to automate this task by developing a real-time facial recognition system, characterized by a face detector, a Keypoint detector, and a convoluted neural network using "DeepLearning" to extract characteristics of the face detected.Una organització del transtorn nutricional ha posat en pràctica un nou tractament per als seus pacients. Aquest tractament està directament relacionat amb l'emoció del pacient, que es classifica oralment. En aquest punt apareix el repte d'automatitzar aquesta feina, dessenvolupant un sistema de reconeixement d'emocions facials en temps real caracteritzat per un detector de cares, un detector de KeyPoints i una xarxa neuronal convolucional utilitzant "DeepLearning" per extraure les característiques de la cara detectada

Who are you? - real-time person identification

This paper presents a system for person identification that uses concise statis-tical models of facial features in a real-time realisation of the cast identifica-tion system of Everingham et al. [7]. Our system integrates the cascaded face detector of Viola and Jones with a kernel-based regressor for face tracking, which is trained on-line when new people are detected in the video stream. A pictorial model is used to compute the locations of facial features, which form a descriptor of the person’s face. When sufficient samples are collected, identification is performed using a random-ferns classifier by marginalising over the facial features. This confers robustness to localisation errors and occlusions, while enabling a real-time search of the database. These four different processes communicate within a real-time framework capable of tracking and identifying up to 5 people in real-time on a standard dual-core 1.86GHz machine.