Unsupervised Feature Descriptors Based Facial Tracking over Distributed Geospatial Subspaces
05 Dec 2017-pp 196-202
TL;DR: This work proposes a system for fast large scale facial tracking over distributed systems beyond individual human capabilities leveraging the computational prowess of large scale processing engines such as Apache Spark.
Abstract: Object Tracking has primarily been characterized as the study of object motion trajectory over constraint subspaces under attempts to mimic human efficiency. However, the trend of monotonically increasing applicability and integrated relevance over distributed commercial frontiers necessitates that scalability be addressed. The present work proposes a system for fast large scale facial tracking over distributed systems beyond individual human capabilities leveraging the computational prowess of large scale processing engines such as Apache Spark. The system is pivoted on an interval based approach for receiving the input feed streams, which is followed by a deep encoder-decoder network for generation of robust environment invariant feature encoding. The system performance is analyzed while functionally varying various pipeline components, to highlight the robustness of the vector representations and near real-time processing performance.
References
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02 Dec 1996
TL;DR: The authors present a real-time face tracker that can track a person's face while the person moves freely in a room and can be applied to teleconferencing and many HCI applications including lip reading and gaze tracking.
Abstract: The authors present a real-time face tracker. The system has achieved a rate of 30+ frames/second using an HP-9000 workstation with a frame grabber and a Canon VC-Cl camera. It can track a person's face while the person moves freely (e.g., walks, jumps, sits down and stands up) in a room. Three types of models have been employed in developing the system. First, they present a stochastic model to characterize skin color distributions of human faces. The information provided by the model is sufficient for tracking a human face in various poses and views. This model is adaptable to different people and different lighting conditions in real-time. Second, a motion model is used to estimate image motion and to predict the search window. Third, a camera model is used to predict and compensate for camera motion. The system can be applied to teleconferencing and many HCI applications including lip reading and gaze tracking. The principle in developing this system can be extended to other tracking problems such as tracking the human hand.
647 citations
"Unsupervised Feature Descriptors Ba..." refers background or methods in this paper
...Eigenfaces, obtained by performing PCA on a set of faces are commonly used [11] to identify faces....
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...Several near real-time systems such as A Real-time face tracker [11], Pfinder [10], patch flow based [9] have been researched and reported with attempts to achieve human like accuracy in effortlessly tracking objects of interest....
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13 Jun 2010
TL;DR: This work proposes a pose-adaptive matching method that uses pose-specific classifiers to deal with different pose combinations of the matching face pair, and finds that a simple normalization mechanism after PCA can further improve the discriminative ability of the descriptor.
Abstract: We present a novel approach to address the representation issue and the matching issue in face recognition (verification). Firstly, our approach encodes the micro-structures of the face by a new learning-based encoding method. Unlike many previous manually designed encoding methods (e.g., LBP or SIFT), we use unsupervised learning techniques to learn an encoder from the training examples, which can automatically achieve very good tradeoff between discriminative power and invariance. Then we apply PCA to get a compact face descriptor. We find that a simple normalization mechanism after PCA can further improve the discriminative ability of the descriptor. The resulting face representation, learning-based (LE) descriptor, is compact, highly discriminative, and easy-to-extract. To handle the large pose variation in real-life scenarios, we propose a pose-adaptive matching method that uses pose-specific classifiers to deal with different pose combinations (e.g., frontal v.s. frontal, frontal v.s. left) of the matching face pair. Our approach is comparable with the state-of-the-art methods on the Labeled Face in Wild (LFW) benchmark (we achieved 84.45% recognition rate), while maintaining excellent compactness, simplicity, and generalization ability across different datasets.
470 citations
"Unsupervised Feature Descriptors Ba..." refers methods in this paper
...Using an implicit algorithm for capturing geometric information encoded into the descriptors, the issue of pose problem and misalignment can be tackled [2]....
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29 Apr 2007
TL;DR: This paper develops several innovative interaction techniques for semi-automatic photo annotation that provide a more user friendly interface for the annotation of person name, location, and event, and thus substantially improve the annotation performance especially for a large photo album.
Abstract: Digital photo management is becoming indispensable for the explosively growing family photo albums due to the rapid popularization of digital cameras and mobile phone cameras. In an effective photo management system photo annotation is the most challenging task. In this paper, we develop several innovative interaction techniques for semi-automatic photo annotation. Compared with traditional annotation systems, our approach provides the following new features: "cluster annotation" puts similar faces or photos with similar scene together, and enables user label them in one operation; "contextual re-ranking" boosts the labeling productivity by guessing the user intention; "ad hoc annotation" allows user label photos while they are browsing or searching, and improves system performance progressively through learning propagation. Our results show that these technologies provide a more user friendly interface for the annotation of person name, location, and event, and thus substantially improve the annotation performance especially for a large photo album.
177 citations
"Unsupervised Feature Descriptors Ba..." refers background in this paper
...Recently, visual representation and tracking has been subject to motivated research owing to increased relevance and interoperability with innumerable application domains such as criminal tracking, object tagging [3] etc....
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Posted Content•
TL;DR: MLlib as discussed by the authors is an open-source distributed machine learning library for Apache Spark that provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives.
Abstract: Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLlib supports several languages and provides a high-level API that leverages Spark's rich ecosystem to simplify the development of end-to-end machine learning pipelines. MLlib has experienced a rapid growth due to its vibrant open-source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed.
84 citations
01 Sep 2009
TL;DR: This work enables both elastic and partial matching by computing a part based face representation and reveals that filtering the face image by a simple difference of Gaussian brings significant robustness to lighting variations and beats the more utilized self-quotient image.
Abstract: We present a robust elastic and partial matching metric for face recognition To handle challenges such as pose, facial expression and partial occlusion, we enable both elastic and partial matching by computing a part based face representation In which N local image descriptors are extracted from densely sampled overlapping image patches We then define a distance metric where each descriptor in one face is matched against its spatial neighborhood in the other face and the minimal distance is recorded For implicit partial matching, the list of all minimal distances are sorted in ascending order and the distance at the αN-th position is picked up as the final distance The parameter 0 ≤ α ≤ 1 controls how much occlusion, facial expression changes, or pixel degradations we would allow The optimal parameter values of this new distance metric are extensively studied and identified with real-life photo collections We also reveal that filtering the face image by a simple difference of Gaussian brings significant robustness to lighting variations and beats the more utilized self-quotient image Extensive evaluations on face recognition benchmarks show that our method is leading or is competitive in performance when compared to state-of-the-art
75 citations
"Unsupervised Feature Descriptors Ba..." refers background in this paper
...Simple elastic and partial metric proposed by Gang can also handle pose change and clutter backgrounds [4]....
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