A comprehensive comparative study of handcrafted methods for face recognition LBP-like and non LBP operators

Raju

et al. 2021

Multimed Tools Appl

Smart city surveillance systems are the battery operated light weight Internet of Things (IoT) devices. In such devices, automatic face recognition requires a low powered memory efficient visual computing system. For these real time applications in smart cities, efficient visual recognition systems are need of the hour. In this manuscript, efficient fast subspace decomposition over Chi Square transformation is proposed for IoT based on smart city surveillance systems. The proposed technique extracts the features for visual recognition using local binary pattern histogram. The redundant features are discarded by applying the fast subspace decomposition over the Gaussian distributed Local Binary Pattern (LBP) features. This redundancy is major contributor to memory and time consumption for battery based surveillance systems. The proposed technique is suitable for all visual recognition applications deployed in IoT based surveillance devices due to higher dimension reduction. The validation of proposed technique is proved on the basis of well-known databases. The technique shows significant results for all databases when implemented on Raspberry Pi. A comparison of the proposed technique with already existing/ reported techniques for the similar applications has been provided. Least error rate is achieved by the proposed technique with maximum feature reduction in minimum time for all the standard databases. Therefore, the proposed algorithm is useful for real time visual recognition for smart city surveillance.

Section: Introductionmentioning

confidence: 99%

An efficient framework using visual recognition for IoT based smart city surveillance

Raju

et al. 2021

Multimed Tools Appl

“…The holistic approach exploits the entire face and projects it into a small subspace such as Eigenfaces in manifold space [45], Fisherfaces [16,33]. The local approach considers certain facial features such as Speed-up robust features (SURF) [17], Local Binary Patterns (LBP) [22]. The local information combines with the holistic information to enrich feature descriptors for performance improvements in the hybrid approach: the fusion of 54 Gabor functions and fuzzy logic for facial expression recognition [15], two-color local descriptors, called Color ZigZag Binary Pattern (CZZBP) [19], or a fusion of Deep features [12].…”

Section: Introductionmentioning

confidence: 99%

Weighted statistical binary patterns for facial feature representation

2021

We present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

“…In addition, it combines structural as well as the statistical features. Because of the advantages of LBP, numerous improved operators have been introduced in recent years [13]- [16], [48], [49]. In order to achieve multi-resolution analysis, Ojala et al [17] extended LBP from 8-neighborhood to arbitrary circular neighborhoods (R, P), where P and R represent the number of sampling points and the radius respectively.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition

Sun

Tang

et al. 2020

IEEE Access

Face recognition has been deeply studied and widely used in recent years. A novel method, called local gradient number pattern (LGNP), is firstly presented in the paper for face description. For LGNP, the Sobel operator is adopted to extract the local gradient information, and the position of the gray transitions in the local neighborhood is used to form the LGNP code based on the LDP-based methods. Then, the concept of fuzzy convex-concave partition (FCCP) is introduced to fuse the global and regional information based on convex-concave partition (CCP). By the combination of LGNP and FCCP, the proposed descriptor is denoted as FCCP_LGNP. To evaluate the performance of FCCP_LGNP comprehensively, a series of experiments were carried out on four different face databases ORL, CALTECH, GEORGIA, and FACE94, and the results show that FCCP_LGNP is superior to the recent state-of-the-art methods based on hand-crafted features. Even compared with the deep learning methods, VGG16 and ResNet101, the proposed descriptor still shows good performance. INDEX TERMS Face recognition, local gradient number pattern, fuzzy convex-concave partition, LDP-based methods.