4 research outputs found
Domain-Specific Face Synthesis for Video Face Recognition from a Single Sample Per Person
The performance of still-to-video FR systems can decline significantly
because faces captured in unconstrained operational domain (OD) over multiple
video cameras have a different underlying data distribution compared to faces
captured under controlled conditions in the enrollment domain (ED) with a still
camera. This is particularly true when individuals are enrolled to the system
using a single reference still. To improve the robustness of these systems, it
is possible to augment the reference set by generating synthetic faces based on
the original still. However, without knowledge of the OD, many synthetic images
must be generated to account for all possible capture conditions. FR systems
may, therefore, require complex implementations and yield lower accuracy when
training on many less relevant images. This paper introduces an algorithm for
domain-specific face synthesis (DSFS) that exploits the representative
intra-class variation information available from the OD. Prior to operation, a
compact set of faces from unknown persons appearing in the OD is selected
through clustering in the captured condition space. The domain-specific
variations of these face images are projected onto the reference stills by
integrating an image-based face relighting technique inside the 3D
reconstruction framework. A compact set of synthetic faces is generated that
resemble individuals of interest under the capture conditions relevant to the
OD. In a particular implementation based on sparse representation
classification, the synthetic faces generated with the DSFS are employed to
form a cross-domain dictionary that account for structured sparsity.
Experimental results reveal that augmenting the reference gallery set of FR
systems using the proposed DSFS approach can provide a higher level of accuracy
compared to state-of-the-art approaches, with only a moderate increase in its
computational complexity
Facial Data Classification Through Enhanced Local Binary Patterns (LBP) and Dynamic Range Local Binary Patterns (DRLBP) Algorithms
A significant amount of reliance is placed on facial data classification in contemporary computer vision and pattern recognition. This research presents a novel method that makes use of the algorithms of Dynamic Range Local Binary Patterns (DRLBP) and Enhanced Local Binary Patterns for the purpose of face data classification that is both effective and precise (LBP). The classic LBP methodology is expanded upon by the Enhanced LBP method, which incorporates adaptive thresholding techniques and spatial histogram characteristics. This makes it possible to conduct a more thorough investigation of the texture and to be resilient in a variety of lighting conditions. By continuously modifying the local binary pattern range, the DRLBP algorithm improves upon this in order to better accommodate nuanced facial features and expressions. This is done in order to better accommodate facial expressions. In terms of accuracy, speed, and adaptability, our proposed system beats state-of-the-art alternatives, as demonstrated by extensive trials conducted on commonly used facial datasets. According to the findings of our investigation, it would appear that human-computer interaction (HCI), digital forensics, and security systems could all stand to gain a great deal from a solution that combines Enhanced LBP and DRLBP algorithms for the classification of face data