Cascaded Facial Detection Algorithms To Improve Recognition

The desire to be able to use computer programs to recognize certain biometric qualities of people have been desired by several different types of organizations. One of these qualities worked on and has achieved moderate success is facial detection and recognition. Being able to use computers to determine where and who a face is has generated several different algorithms to solve this problem with different benefits and drawbacks. At the backbone of each algorithm is the desire for it to be quick and accurate. By cascading face detection algorithms, accuracy can be improved but runtime will subsequently be increased. Neural networks, once trained, have the ability to quickly categorize objects and assign them identifiers. Combining cascaded face detectors and neural networks, a face in an image can be detected and recognized. In this paper, three different types of facial detection algorithms are combined in various configurations to test the accuracy of face detection at the cost of runtime. By feeding these faces into a convolution neural network, we can begin identifying who the person is

A novel integration of face-recognition algorithms with a soft voting scheme for efficiently tracking missing person in challenging large-gathering scenarios

The probability of losing vulnerable companions, such as children or older ones, in large gatherings is high, and their tracking is challenging. We proposed a novel integration of face-recognition algorithms with a soft voting scheme, which was applied, on low-resolution cropped images of detected faces, in order to locate missing persons in a challenging large-crowd gathering. We considered the large-crowd gathering scenarios at Al Nabvi mosque Madinah. It is a highly uncontrolled environment with a low-resolution-images data set gathered from moving cameras. The proposed model first performs real-time face-detection from camera-captured images, and then it uses the missing person’s profile face image and applies well-known face-recognition algorithms for personal identification, and their predictions are further combined to obtain more mature prediction. The presence of a missing person is determined by a small set of consecutive frames. The novelty of this work lies in using several recognition algorithms in parallel and combining their predictions by a unique soft-voting scheme, which in return not only provides a mature prediction with spatio-temporal values but also mitigates the false results of individual recognition algorithms. The experimental results of our model showed reasonably good accuracy of missing person’s identification in an extremely challenging large-gathering scenario

Information theoretic combination of classifiers with application to face detection

Combining several classifiers has become a very active subdiscipline in the field of pattern recognition. For years, pattern recognition community has focused on seeking optimal learning algorithms able to produce very accurate classifiers. However, empirical experience proved that is is often much easier finding several relatively good classifiers than only finding one single very accurate predictor. The advantages of combining classifiers instead of single classifier schemes are twofold: it helps reducing the computational requirements by using simpler models, and it can improve the classification skills. It is commonly admitted that classifiers need to be complementary in order to improve their performances by aggregation. This complementarity is usually termed as diversity in classifier combination community. Although diversity is a very intuitive concept, explicitly using diversity measures for creating classifier ensembles is not as successful as expected. In this thesis, we propose an information theoretic framework for combining classifiers. In particular, we prove by means of information theoretic tools that diversity between classifiers is not sufficient to guarantee optimal classifier combination. In fact, we show that diversity and accuracies of the individual classifiers are generally contradictory: two very accurate classifiers cannot be diverse, and inversely, two very diverse classifiers will necessarily have poor classification skills. In order to tackle this contradiction, we propose a information theoretic score (ITS) that fixes a trade-off between these two quantities. A first possible application is to consider this new score as a selection criterion for extracting a good ensemble in a predefined pool of classifiers. We also propose an ensemble creation technique based on AdaBoost, by taking into account the information theoretic score for iteratively selecting the classifiers. As an illustration of efficient classifier combination technique, we propose several algorithms for building ensembles of Support Vector Machines (SVM). Support Vector Machines are one of the most popular discriminative approaches of pattern recognition and are often considered as state-of-the-art in binary classification. However these classifiers present one severe drawback when facing a very large number of training examples: they become computationally expensive to train. This problem can be addressed by decomposing the learning into several classification tasks with lower computational requirements. We propose to train several parallel SVM on subsets of the complete training set. We develop several algorithms for designing efficient ensembles of SVM by taking into account our information theoretic score. The second part of this thesis concentrates on human face detection, which appears to be a very challenging binary pattern recognition task. In this work, we focus on two main aspects: feature extraction and how to apply classifier combination techniques to face detection systems. We introduce new geometrical filters called anisotropic Gaussian filters, that are very efficient to model face appearance. Finally we propose a parallel mixture of boosted classifier for reducing the false positive rate and decreasing the training time, while keeping the testing time unchanged. The complete face detection system is evaluated on several datasets, showing that it compares favorably to state-of-the-art techniques

Automatic face alignment by maximizing similarity score

Accurate face registration is of vital importance to the performance of a face recognition algorithm. We propose a face registration method which searches for the optimal alignment by maximizing the score of a face recognition algorithm. In this paper we investigate the practical usability of our face registration method. Experiments show that our registration method achieves better results in face verification than the landmark based registration method. We even obtain face verification results which are similar to results obtained using landmark based registration with manually located eyes, nose and mouth as landmarks. The performance of the method is tested on the FRGCv1 database using images taken under both controlled and uncontrolled conditions

Model based methods for locating, enhancing and recognising low resolution objects in video

Visual perception is our most important sense which enables us to detect and recognise objects even in low detail video scenes. While humans are able to perform such object detection and recognition tasks reliably, most computer vision algorithms struggle with wide angle surveillance videos that make automatic processing difficult due to low resolution and poor detail objects. Additional problems arise from varying pose and lighting conditions as well as non-cooperative subjects. All these constraints pose problems for automatic scene interpretation of surveillance video, including object detection, tracking and object recognition.Therefore, the aim of this thesis is to detect, enhance and recognise objects by incorporating a priori information and by using model based approaches. Motivated by the increasing demand for automatic methods for object detection, enhancement and recognition in video surveillance, different aspects of the video processing task are investigated with a focus on human faces. In particular, the challenge of fully automatic face pose and shape estimation by fitting a deformable 3D generic face model under varying pose and lighting conditions is tackled. Principal Component Analysis (PCA) is utilised to build an appearance model that is then used within a particle filter based approach to fit the 3D face mask to the image. This recovers face pose and person-specific shape information simultaneously. Experiments demonstrate the use in different resolution and under varying pose and lighting conditions. Following that, a combined tracking and super resolution approach enhances the quality of poor detail video objects. A 3D object mask is subdivided such that every mask triangle is smaller than a pixel when projected into the image and then used for model based tracking. The mask subdivision then allows for super resolution of the object by combining several video frames. This approach achieves better results than traditional super resolution methods without the use of interpolation or deblurring.Lastly, object recognition is performed in two different ways. The first recognition method is applied to characters and used for license plate recognition. A novel character model is proposed to create different appearances which are then matched with the image of unknown characters for recognition. This allows for simultaneous character segmentation and recognition and high recognition rates are achieved for low resolution characters down to only five pixels in size. While this approach is only feasible for objects with a limited number of different appearances, like characters, the second recognition method is applicable to any object, including human faces. Therefore, a generic 3D face model is automatically fitted to an image of a human face and recognition is performed on a mask level rather than image level. This approach does not require an initial pose estimation nor the selection of feature points, the face alignment is provided implicitly by the mask fitting process

Towards robust and reliable multimedia analysis through semantic integration of services

Thanks to ubiquitous Web connectivity and portable multimedia devices, it has never been so easy to produce and distribute new multimedia resources such as videos, photos, and audio. This ever-increasing production leads to an information overload for consumers, which calls for efficient multimedia retrieval techniques. Multimedia resources can be efficiently retrieved using their metadata, but the multimedia analysis methods that can automatically generate this metadata are currently not reliable enough for highly diverse multimedia content. A reliable and automatic method for analyzing general multimedia content is needed. We introduce a domain-agnostic framework that annotates multimedia resources using currently available multimedia analysis methods. By using a three-step reasoning cycle, this framework can assess and improve the quality of multimedia analysis results, by consecutively (1) combining analysis results effectively, (2) predicting which results might need improvement, and (3) invoking compatible analysis methods to retrieve new results. By using semantic descriptions for the Web services that wrap the multimedia analysis methods, compatible services can be automatically selected. By using additional semantic reasoning on these semantic descriptions, the different services can be repurposed across different use cases. We evaluated this problem-agnostic framework in the context of video face detection, and showed that it is capable of providing the best analysis results regardless of the input video. The proposed methodology can serve as a basis to build a generic multimedia annotation platform, which returns reliable results for diverse multimedia analysis problems. This allows for better metadata generation, and improves the efficient retrieval of multimedia resources

Combining textual and visual information processing for interactive video retrieval: SCHEMA's participation in TRECVID 2004

In this paper, the two different applications based on the Schema Reference System that were developed by the SCHEMA NoE for participation to the search task of TRECVID 2004 are illustrated. The first application, named ”Schema-Text”, is an interactive retrieval application that employs only textual information while the second one, named ”Schema-XM”, is an extension of the former, employing algorithms and methods for combining textual, visual and higher level information. Two runs for each application were submitted, I A 2 SCHEMA-Text 3, I A 2 SCHEMA-Text 4 for Schema-Text and I A 2 SCHEMA-XM 1, I A 2 SCHEMA-XM 2 for Schema-XM. The comparison of these two applications in terms of retrieval efficiency revealed that the combination of information from different data sources can provide higher efficiency for retrieval systems. Experimental testing additionally revealed that initially performing a text-based query and subsequently proceeding with visual similarity search using one of the returned relevant keyframes as an example image is a good scheme for combining visual and textual information