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52
Online Domain Adaptation of a Pre-Trained Cascade of Classifiers
"... Many classifiers are trained with massive training sets only to be applied at test time on data from a different distribution. How can we rapidly and simply adapt a classifier to a new test distribution, even when we do not have access to the original training data? We present an on-line approach fo ..."
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Cited by 44 (1 self)
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Many classifiers are trained with massive training sets only to be applied at test time on data from a different distribution. How can we rapidly and simply adapt a classifier to a new test distribution, even when we do not have access to the original training data? We present an on-line approach for rapidly adapting a “black box ” classifier to a new test data set without retraining the classifier or examining the original optimization criterion. Assuming the original classifier outputs a continuous number for which a threshold gives the class, we reclassify points near the original boundary using a Gaussian process regression scheme. We show how this general procedure can be used in the context of a classifier cascade, demonstrating performance that far exceeds state-of-the-art results in face detection on a standard data set. We also draw connections to work in semi-supervised learning, domain adaptation, and information regularization. 1.
Optimal landmark detection using shape models and branch and bound
- In Proc. ICCV
"... Fitting statistical 2D and 3D shape models to images is necessary for a variety of tasks, such as video editing and face recognition. Much progress has been made on local fitting from an initial guess, but determining a close enough initial guess is still an open problem. One approach is to de-tect ..."
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Cited by 13 (0 self)
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Fitting statistical 2D and 3D shape models to images is necessary for a variety of tasks, such as video editing and face recognition. Much progress has been made on local fitting from an initial guess, but determining a close enough initial guess is still an open problem. One approach is to de-tect distinct landmarks in the image and initalize the model fit from these correspondences. This is difficult, because de-tection of landmarks based only on the local appearance is inherently ambiguous. This makes it necessary to use global shape information for the detections. We propose a method to solve the combinatorial problem of selecting out of a large number of candidate landmark detections the configuration which is best supported by a shape model. Our method, as opposed to previous approaches, always finds the globally optimal configuration. The algorithm can be applied to a very general class of shape models and is independent of the underlying feature point detector. Its theoretic optimality is shown, and it is evaluated on a large face dataset. 1. Introduction and
Probabilistic elastic part model for unsupervised face detector adaptation
- In ICCV
"... We propose an unsupervised detector adaptation algo-rithm to adapt any offline trained face detector to a specific collection of images, and hence achieve better accuracy. The core of our detector adaptation algorithm is a prob-abilistic elastic part (PEP) model, which is offline trained with a set ..."
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Cited by 13 (2 self)
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We propose an unsupervised detector adaptation algo-rithm to adapt any offline trained face detector to a specific collection of images, and hence achieve better accuracy. The core of our detector adaptation algorithm is a prob-abilistic elastic part (PEP) model, which is offline trained with a set of face examples. It produces a statistically-aligned part based face representation, namely the PEP representation. To adapt a general face detector to a col-lection of images, we compute the PEP representations of the candidate detections from the general face detector, and then train a discriminative classifier with the top positives and negatives. Then we re-rank all the candidate detections with this classifier. This way, a face detector tailored to the statistics of the specific image collection is adapted from the original detector. We present extensive results on three datasets with two state-of-the-art face detectors. The signif-icant improvement of detection accuracy over these state-of-the-art face detectors strongly demonstrates the efficacy of the proposed face detector adaptation algorithm. 1.
Detecting and aligning faces by image retrieval
- In CVPR
"... Detecting faces in uncontrolled environments continues to be a challenge to traditional face detection methods[24] due to the large variation in facial appearances, as well as occlusion and clutter. In order to overcome these challenges, we present a novel and robust exemplar-based face detector tha ..."
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Cited by 13 (2 self)
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Detecting faces in uncontrolled environments continues to be a challenge to traditional face detection methods[24] due to the large variation in facial appearances, as well as occlusion and clutter. In order to overcome these challenges, we present a novel and robust exemplar-based face detector that integrates image retrieval and discriminative learning. A large database of faces with bounding rectangles and facial landmark locations is collected, and simple discriminative classifiers are learned from each of them. A voting-based method is then proposed to let these classifiers cast votes on the test image through an efficient image retrieval technique. As a result, faces can be very efficiently detected by selecting the modes from the voting maps, without resorting to exhaustive sliding window-style scanning. Moreover, due to the exemplar-based framework, our approach can detect faces under challenging conditions without explicitly modeling their variations. Evaluation on two public benchmark datasets shows that our new face detection approach is accurate and efficient, and achieves the state-of-the-art performance. We further propose to use image retrieval for face validation (in order to remove false positives) and for face alignment/landmark localization. The same methodology can also be easily generalized to other face-related tasks, such as attribute recognition, as well as general object detection. 1.
Supervised sequential classification under budget constraints
- In AISTATS
, 2013
"... In this paper we develop a framework for a sequential decision making under budget constraints for multi-class classification. In many classification systems, such as medical diagnosis and homeland security, sequential decisions are often warranted. For each in-stance, a sensor is first chosen for a ..."
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Cited by 10 (5 self)
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In this paper we develop a framework for a sequential decision making under budget constraints for multi-class classification. In many classification systems, such as medical diagnosis and homeland security, sequential decisions are often warranted. For each in-stance, a sensor is first chosen for acquir-ing measurements and then based on the available information one decides (rejects) to seek more measurements from a new sen-sor/modality or to terminate by classifying the example based on the available informa-tion. Different sensors have varying costs for acquisition, and these costs account for delay, throughput or monetary value. Con-sequently, we seek methods for maximizing performance of the system subject to bud-get constraints. We formulate a multi-stage multi-class empirical risk objective and learn sequential decision functions from training data. We show that reject decision at each stage can be posed as supervised binary clas-sification. We derive bounds for the VC di-mension of the multi-stage system to quan-tify the generalization error. We compare our approach to alternative strategies on several multi-class real world datasets. 1
Facial Expression Analysis
"... The face is one of the most powerful channels of nonverbal communication. Facial expression provides cues about emotion, intention, alertness, pain, personality, regulates interpersonal behavior, and communicates psychiatric and biomedical status among other functions. Within the past 15 years, the ..."
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Cited by 8 (2 self)
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The face is one of the most powerful channels of nonverbal communication. Facial expression provides cues about emotion, intention, alertness, pain, personality, regulates interpersonal behavior, and communicates psychiatric and biomedical status among other functions. Within the past 15 years, there has been increasing interest in automated facial expression analysis within the computer vision and machine learning communities. This chapter reviews fundamental approaches to facial measurement by behavioral scientists and current efforts in automated facial expression recognition. We consider challenges, review databases available to the research community, approaches to feature detection, tracking, and representation, and both supervised and unsupervised learning.
Learning SURF Cascade for Fast and Accurate Object Detection
"... This paper presents a novel learning framework for training boosting cascade based object detector from large scale dataset. The framework is derived from the well-known Viola-Jones (VJ) framework but distinguished by three key differences. First, the proposed framework adopts multi-dimensional SURF ..."
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Cited by 8 (0 self)
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This paper presents a novel learning framework for training boosting cascade based object detector from large scale dataset. The framework is derived from the well-known Viola-Jones (VJ) framework but distinguished by three key differences. First, the proposed framework adopts multi-dimensional SURF features instead of single dimen-sional Haar features to describe local patches. In this way, the number of used local patches can be reduced from hun-dreds of thousands to several hundreds. Second, it adopts logistic regression as weak classifier for each local patch instead of decision trees in the VJ framework. Third, we adopt AUC as a single criterion for the convergence test during cascade training rather than the two trade-off cri-teria (false-positive-rate and hit-rate) in the VJ framework. The benefit is that the false-positive-rate can be adaptive among different cascade stages, and thus yields much faster convergence speed of SURF cascade. Combining these points together, the proposed approach has three good properties. First, the boosting cascade can be trained very efficiently. Experiments show that the pro-posed approach can train object detectors from billions of negative samples within one hour even on personal comput-ers. Second, the built detector is comparable to the state-of-the-art algorithm not only on the accuracy but also on the processing speed. Third, the built detector is small in model-size due to short cascade stages. 1.
Multi-stage classifier design
- Machine Learning
"... In many classification systems, sensing modalities have different acquisition costs. It is often unnecessary to use every modality to classify a majority of examples. We study a multi-stage system in a prediction time cost reduction setting, where the full data is avail-able for training, but for a ..."
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Cited by 7 (1 self)
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In many classification systems, sensing modalities have different acquisition costs. It is often unnecessary to use every modality to classify a majority of examples. We study a multi-stage system in a prediction time cost reduction setting, where the full data is avail-able for training, but for a test example, measurements in a new modality can be acquired at each stage for an additional cost. We seek decision rules to reduce the average mea-surement acquisition cost. We formulate an empirical risk minimization problem (ERM) for a multi-stage reject classifier, wherein the stage k classifier either classifies a sample using only the measurements acquired so far or rejects it to the next stage where more attributes can be acquired for a cost. To solve the ERM problem, we factorize the cost function into classification and rejection decisions. We then transform reject decisions into a binary classification problem. We construct stage-by-stage global surrogate risk, develop an iterative algorithm in the boosting framework and present convergence results. We test our work on synthetic, medical and explosives detection datasets. Our results demonstrate that substantial cost reduction without a significant sacrifice in accuracy is achievable.
Combining Haar feature and skin color based classifiers for face detection
- in [IEEE International Conference on Acoustics, Speech and Signal Processing
, 2011
"... This paper presents a hybrid method for face detection in color im-ages. The well known Haar feature-based face detector developed by Viola and Jones (VJ), that has been designed for gray-scale images is combined with a skin-color filter, which provides complementary information in color images. The ..."
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Cited by 5 (0 self)
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This paper presents a hybrid method for face detection in color im-ages. The well known Haar feature-based face detector developed by Viola and Jones (VJ), that has been designed for gray-scale images is combined with a skin-color filter, which provides complementary information in color images. The image is first passed through a Haar-Feature based face detector, which is adjusted such that it is op-erating at a point on its ROC curve that has a low number of missed faces but a high number of false detections. Then, using the proposed skin color post-filtering method many of these false detections can be eliminated easily. We also use a color compensation algorithm to reduce the effects of lighting. Our experimental results on the Bao color face database show that the proposed method is superior to the original VJ algorithm and also to other skin color based pre-filtering methods in the literature in terms of precision. Index Terms — face detection, skin color detection, adaboost, haar features 1.
A Monte Carlo Strategy to Integrate Detection and Model-Based Face Analysis
"... Abstract. We present a novel probabilistic approach for fitting a sta-tistical model to an image. A 3D Morphable Model (3DMM) of faces is interpreted as a generative (Top-Down) Bayesian model. Random Forests are used as noisy detectors (Bottom-Up) for the face and facial landmark positions. The Top- ..."
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Cited by 2 (1 self)
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Abstract. We present a novel probabilistic approach for fitting a sta-tistical model to an image. A 3D Morphable Model (3DMM) of faces is interpreted as a generative (Top-Down) Bayesian model. Random Forests are used as noisy detectors (Bottom-Up) for the face and facial landmark positions. The Top-Down and Bottom-Up parts are then combined us-ing a Data-Driven Markov Chain Monte Carlo Method (DDMCMC). As core of the integration, we use the Metropolis-Hastings algorithm which has two main advantages. First, the algorithm can handle unreliable de-tections and therefore does not need the detectors to take an early and possible wrong hard decision before fitting. Second, it is open for integra-tion of various cues to guide the fitting process. Based on the proposed approach, we implemented a completely automatic, pose and illumina-tion invariant face recognition application. We are able to train and test the building blocks of our application on different databases. The sys-tem is evaluated on the Multi-PIE database and reaches state of the art performance. 1
