Texture Features in Facial Image Analysis

Abstract

Abstract. While features used for texture analysis have been successfully used in some biometric applications, only quite few works have considered them in facial image analysis. Texture-based region descriptors can be very useful in recognizing faces and facial expressions, detecting faces and different facial components, and in other face related tasks. This paper demonstrates this issue by considering the local binary pattern (LBP) as an example of texture-based approach and showing its efficiency in facial image analysis.

Secure Multimodal Biometric Recognition Based on Speech, Face and Fingerprint Using Feature Level Fusion Approach

“…The utilizations of facial recognition range from a static, controlled "mug-shot" confirmation to a dynamic, uncontrolled face recognizable proof in a jumbled foundation [3]. The most well-known ways to deal with face recognition depend on either: 1) the area and state of facial characteristics, for example, the eyes, eyebrows, nose, lips and button, and their spatial connections, or 2) the overall (worldwide) examination of the face picture that speaks to a face as a weighted combination of a number of canonical faces [4].…”

Rapid Damage Assessment from High Resolution Imagery

“…LBP based features have been used in various applications like face detection, image analysis and image retrieval because of its better tolerance to illumination changes. The LBP is computed by using a moving window operator and producing a binary pattern by thresholding the window elements by the center pixel [6]. The binary pattern is assigned to the center pixel The LEP is similar to the LBP but extracted from edge maps rather than pixel intensity values [1].…”

Assessing the match performance of non-ideal operational facial images using 3D image data.

“…The local binary pattern (LBP) texture method has proved to be an excellent technique can be utilized in real-time applications [52]. This is because of its computational simplicity and robustness to illumination changes, unlike other methods.…”

“…A facial image is divided into numerous small windows and the histogram of LBP in each window is calculated. Local primitives codified by these bins include different types of curved edges, spots, flat areas etc [52]…”

“…Multiscale LBP is an extension to basic LBP. In general, for the operator represented as LBP P, R refers to a neighborhood size of P equally spaced pixels on a circle of radius R that form a circularly symmetric neighbor set[52]. The number of bins in the histogram include 8 and 2P for LBP and MLBP respectively.…”