User oriented language model for face detection

Abstract

This paper provides a novel approach for a user oriented language model for face detection. Even though there are many open source or commercial libraries to solve the problem of face detection, they are still hard to use because they require specific knowledge on details of algorithmic techniques. This paper proposes a high-level language model for face detection with which users can develop systems easily and even without specific knowledge on face detection theories and algorithms. Important conditions are firstly considered to categorize the large problem space of face detection. The conditions identified here are then represented as expressions in terms of a language model so that developers can use them to express various problems. Once the conditions are expressed by users, the proposed associated interpreter interprets the conditions to find and organize the best algorithms to solve the represented problem with corresponding conditions. We show a proof-of-concept implementation and some test and analyze example problems to show the ease of use and usability.

Face Detection and Recognition Techniques: A Survey

“…Face detection is often used as a preprocessing for identifying persons by providing the exact future improvement, the technique need more face detection algorithms will be analyzed and added for more practical and better usability of the language model. Some intelligent approaches for selecting algorithms are necessary to be considered for more optimal selection process [9]. The proposed approach, at first, they detect the face using Viola and Jones' Boosting algorithm and a set of Haar-like cascade features.…”

An Abstraction for Correspondence Search Using Task-Based Controls

“…Declarative programming languages have also been used to provide vision functionality in small, usable units, e.g. ShapeLogic 4 or FVision [12], although they are limited in scope due to the difficulty of combining logic systems with computer vision. While these methods provide a simpler method to access and apply methods, there is no abstraction above the algorithmic level, and so users of these frameworks must have a sophisticated knowledge of vision to apply them effectively.…”

“…This idea has been applied successfully in many other fields, notably graphics with OpenGL, and is the main goal of OpenVL [1] for computer vision. OpenVL is an abstraction framework which hides algorithmic detail and provides developers with access to sophisticated vision methods, such as segmentation [2], human body pose estimation [3] and face detection [4]. The work presented here applies a similar methodology to construct a task-based description of correspondence search at a low enough level to maintain flexibility but high enough for mainstream developers to apply successfully, within the OpenVL framework.…”

OpenVL: A task-based abstraction for developer-friendly computer vision

“…We use face detection to illustrate our approach: unlike frameworks such as OpenCV [3] (which uses algorithm-level APIs -the function to detect faces is called cvHaarDetect) we allow the developer to provide the parameters of a face and the interpreter chooses a method capable of finding the described face. Our description is based on a prior examination of the face detection problem: Jang et al [8] presented a taxonomy of face detection methods using an arrangement based on the problem conditions. Their description provided parameters for pose (2D and 3D), size, occlusion, gender, age, expression and illumination.…”