This study investigates reverse image search performance of Google, in terms of Average Precisions (APs) at various cut-off points, on finding out similar images by using fresh Image Queries (IQs) from the five categories "Fashion", "Computer", "Home", "Sports", and "Toys", in order to have an insight about reverse image search performance of Google and then, motivate the researchers and inform the users. Five fresh IQs with different main concepts were created for each of the five categories. These 25 IQs were run on the search engine and for each, the first 100 images retrieved were evaluated with binary relevance judgment. APs at the cut-off points 20, 40, 60, 80, and 100 were calculated for each category and for all 25 IQs. The performance range is from ~42% for Toys category at the cut-off point 100 to 71% for Home category at the cut-off point 20. When the categories are ignored, Google's performance range is from ~52% at the cut-off point 100 to ~57% at the cut-off point 20. It seems that reverse image search performance of Google needs to be improved.

Abstract This study aims to measure the efficiency of ear and profile face in distinguishing identical twins under identification and verification modes. In addition, to distinguish identical twins by ear and profile face separately, we propose to fuse these traits with all possible binary combinations of left ear, left profile face, right ear, and right profile face. Fusion is implemented by score‐level fusion and decision‐level fusion techniques in the proposed method. Additionally, feature‐level fusion is used for comparison. All experiments in this paper are also implemented on nontwins individuals, and the recognition performance of twins and nontwins are compared. Local binary patterns, local phase quantization, and binarized statistical image features approaches are used as texture‐based descriptors for feature extraction process. Images under controlled and uncontrolled lighting are tested. Ear and profile images from ND‐TWINS‐2009‐2010 dataset are used in the experiments. The experimental results show that the proposed method is more accurate and reliable than using ear or profile face images separately. The performance of the proposed method for recognizing identical twins as recognition rate is 100% and 99.45%, and equal error rates are 0.54% and 1.63% in controlled and uncontrolled illumination conditions, respectively.

A real‐world animal biometric system that detects and describes animal life in image and video data is an emerging subject in machine vision. These systems develop computer vision approaches for the classification of animals. A novel method for animal face classification based on score‐level fusion of recently popular convolutional neural network (CNN) features and appearance‐based descriptor features is presented. This method utilises a score‐level fusion of two different approaches; one uses CNN which can automatically extract features, learn and classify them; and the other one uses kernel Fisher analysis (KFA) for its feature extraction phase. The proposed method may also be used in other areas of image classification and object recognition. The experimental results show that automatic feature extraction in CNN is better than other simple feature extraction techniques (both local‐ and appearance‐based features), and additionally, appropriate score‐level combination of CNN and simple features can achieve even higher accuracy than applying CNN alone. The authors showed that the score‐level fusion of CNN extracted features and appearance‐based KFA method have a positive effect on classification accuracy. The proposed method achieves 95.31% classification rate on animal faces which is significantly better than the other state‐of‐the‐art methods.

This paper proposes a 2D ear recognition approach that is based on the fusion of ear and tragus using score-level fusion strategy. An attempt to overcome the effect of partial occlusion, pose variation and weak illumination challenges is done since the accuracy of ear recognition may be reduced if one or more of these challenges are available. In this study, the effect of the aforementioned challenges is estimated separately, and many samples of ear that are affected by two different challenges concurrently are also considered. The tragus is used as a biometric trait because it is often free from occlusion; it also provides discriminative features even in different poses and illuminations. The features are extracted using local binary patterns and the evaluation has been done on three datasets of USTB database. It has been observed that the fusion of ear and tragus can improve the recognition performance compared to the unimodal systems. Experimental results show that the proposed method enhances the recognition rates by fusion of parts that are nonoccluded with tragus in the cases of partial occlusion, pose variation and weak illumination. It is observed that the proposed method performs better than feature-level fusion methods and most of the state-of-the-art ear recognition systems.

The authors propose Geometric, texture and color based leaf classification, a novel leaf classification method using a combination of geometric, shape, texture and colour features that are extracted from the photographic image of leaves. This method combines features that complement each other to define the leaf. A new local binary pattern (LBP) variant, namely sorted uniform LBP (LBP P,R su2 ), is also proposed for leaf texture description. The experiments show that LBP P,R su2 has a higher accuracy in leaf texture classification compared with classical rotation invariant LBP variants. In addition, mean and deviation of hue channel International Commission on Illumination – lightness, chroma, hue colour models – are used to describe the colour tone of the leaf. The proposed feature descriptors require a classifier that can prioritise different features. In this study, linear discriminant classifier (LDC) is employed because of its prioritisation and generalisation abilities. The results show that the proposed combination of features classified with LDC outperforms all well‐known leaf classification methods.

This paper proposes hybrid approaches based on both feature level and score level fusion strategies to provide a robust recognition system against the distortions of individual modalities. In order to compare the proposed schemes, a virtual multimodal database is formed from FERET face and PolyU palmprint databases. The proposed hybrid systems concatenate features extracted by local and global feature extraction methods such as Local Binary Patterns, Log Gabor, Principal Component Analysis and Linear Discriminant Analysis. Match score level fusion is performed in order to show the effectiveness and accuracy of the proposed schemes. The experimental results based on these databases reported a significant improvement of the proposed schemes compared with unimodal systems and other multimodal face–palmprint fusion methods.

This paper presents geometric feature-based model for age group classification of facial images. The feature extraction is performed considering significance of the effects that age has on facial anthropometry. Particle Swarm Optimization (PSO) technique is used to find optimized subset of geometric features. The relevance and importance of age differentiation capability of the features are evaluated using support vector classifier. The facial images are categorized in seven major age groups. The effectiveness and accuracy of the proposed feature extraction is demonstrated with the experiments that are conducted on two publicly available databases namely Face and Gesture Recognition Research Network (FGNET) Aging Database and Iranian Face Database (IFDB). The results demonstrate that the success rate of the classification is 92.62%. The results also show significant improvement compared to the state-of-the-art models.

Interpolation‐Based Impulse Noise Removal (IBINR), a fast and simple algorithm is proposed to remove fixed valued impulse noise in this study. The proposed method removes all noisy pixels from the image and determines their values using non‐linear interpolation. Compared with the state‐of‐the‐art noise removal algorithms, IBINR has the highest or comparable performance in terms of photographic image denoising power and resource efficiency: it runs in shorter amount of time and does not have any significant additional memory requirements due to the fact that costly sorting operations are avoided.

In this paper, a new approach based on score level fusion is presented to obtain a robust recognition system by concatenating face and iris scores of several standard classifiers. The proposed method concatenates face and iris match scores instead of concatenating features as in feature-level fusion. The features from face and iris are extracted using local and global feature extraction methods such as PCA, subspace LDA, spPCA, mPCA and LBP. Transformation-based score fusion and classifier-based score fusion are then involved in the process to obtain, concatenate and classify the matching scores. Different fusion techniques at matching score level, feature level and decision level are compared with the proposed method to emphasize improvement and effectiveness of the proposed method. In order to validate the proposed scheme, a combined database is formed using ORL and BANCA face databases together with CASIA and UBIRIS iris databases. The results based on recognition performance and ROC analysis demonstrate that the proposed score level fusion achieves a significant improvement over unimodal methods and other multimodal face-iris fusion methods.