Invariant Local Binary Pattern Feature Research Articles

Improved deep generative adversarial network with illuminant invariant local binary pattern features for facial expression recognition

ABSTRACT Facial Expression Recognition (FER) is one of the prevailing as well as demanding tasks in social communication. In general, face expressions are usual and straight ways for individuals to converse their intentions as well as emotions. This paper proposes the Taylor-Chicken Swarm Optimization-based Deep Generative Adversarial Network (Taylor-CSO-based Deep GAN) for FER. Here, the facial expressions are predicted through a series of steps, such as video frame extraction, pre-processing, face detection, feature extraction, as well as FER. The major contribution of the proposed work lies in the last step, where Taylor-CSO based Deep GAN is employed for recognizing facial expressions. Initially, video frames are extracted from the input video as well as pre-processing is done to extract the Region of Interest (RoI). Then, the Viola Jones algorithm is employed to detect the face images, and Illuminant Invariant Local Binary Pattern (IILBP) features are extracted. Finally, the FER is performed by the proposed model. The performance of the developed model is analyzed using the CK+ dataset, RAVDESS dataset, and SAVEE dataset. Also, the developed model’s performance is evaluated with conventional FER methods. The performance analysis exhibits that the adopted model is precise and valuable.

Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine.

Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and corn seedling classification in the corn seedling stage was established. Second, many different features of corn seedlings and weeds were extracted, and dimensionality was reduced by principal component analysis, including the histogram of oriented gradient feature, rotation invariant local binary pattern (LBP) feature, Hu invariant moment feature, Gabor feature, gray level co-occurrence matrix, and gray level-gradient co-occurrence matrix. Then, the classifier training based on SVM was conducted to obtain the recognition model for corn seedlings and weeds. The comprehensive recognition performance of single feature or different fusion strategies for six features is compared and analyzed, and the optimal feature fusion strategy is obtained. Finally, by utilizing the actual corn seedling field images, the proposed weed and corn seedling detection method effect was tested. LAB color space and K-means clustering were used to achieve image segmentation. Connected component analysis was adopted to remove small objects. The previously trained recognition model was utilized to identify and label each connected region to identify and detect weeds and corn seedlings. The experimental results showed that the fusion feature combination of rotation invariant LBP feature and gray level-gradient co-occurrence matrix based on SVM classifier obtained the highest classification accuracy and accurately detected all kinds of weeds and corn seedlings. It provided information on weed and crop positions to the spraying herbicide robot for accurate spraying or to the precise fertilization machine for accurate fertilizing.

Illumination Invariant Face Recognition Using Near-Infrared Images

Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR image-based face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups.