dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, we used Log-Gabor filters in the preprocessing to increase the contrast of palmprint features. Then, we reduced the number of features using feature selection and dimensionality reduction procedures. For the multimodal system, we fused modalities at the matching score level to improve system performance. The proposed method effectively improves the accuracy of the PalmNet and reduces the number of features as well the computational time. We validated the proposed method on four public palmprint databases, two multispectral databases, CASIA and PolyU, and two contactless databases, Tongji and PolyU 2D/3D. Experiments show that our approach achieves a high recognition rate while using a substantially lower number of features.
To validate our method, we applied it to several public palmprint databases containing images of distinct qualities, resolutions, and dynamic ranges. We also carried out comparisons with several recent state-of-the-art methods. The main contributions of this work are as follows: -The development of effective unimodal and multimodal biometric systems for palmprint recognition. -Improvement of palmprint images using log-Gabor filters by adjusting pixel luminance. -Using feature selection with dimensionality reduction significantly reduced the features vector size with a reduction rate of 0. 003 %, which allowed reducing computational time without degrading the performance of our recognition systems. -Employing the SVM classifier instead of the 1-NN classifier as in the paper  [17]. Experimental results demonstrate that our methodology scored a higher recognition accuracy than existing approaches in the literature.
The rest of the paper is organized as follows: Sect. 2 summarizes earlier works on biometric systems based on multispectral and contactless palmprint databases. Section 3 introduces the proposed approach of unimodal and multimodal identification systems for palmprint recognition. The experimental results are given in Sect. 4, which reports the obtained experimental results. Finally, Sect. 5  concludes the paper. To facilitate reading, Table  1  presents the list of abbreviations and definitions.
First, a hierarchical enhancement algorithm is adopted, which combines a DOG filter and histogram equalization to alleviate uneven illumination and highlights vein textures. Second, a Root Scale Invariant Feature Transform (RootSIFT), a more stable local invariant feature extraction method compared to Scale Invariant Feature Transform (SIFT), is used to overcome the projection transformation in contact-free mode. Recently, many systems and applications have used deep learning for biometric identification. The deep network is trained on a variety of patterns. Once the network has learned all the unique features of the dataset, it can be used to recognize similar patterns. Deep learning approaches have been used primarily to learn features for palmprint recognition. Deep learning can also be very efficient in classification (supervised learning) and clustering (unsupervised learning) tasks.
Then Principal Component Analysis (PCA) is applied to reduce the dimensionality of the data. For classification, a multi-class SVM and the nearest neighbor classifier are used. Svoboda  [28]  proposed a Convolutional Neural Network (CNN) based on the AlexNet model and trained by optimizing a loss function related to the d-prime index to achieve a better genuine/ impostor score distribution separation of touchless palmprint databases. Meraoumia et al. [14]  proposed Principal Component Analysis Network (PCANet) deep learningbased feature extraction using two stages. Then four classifiers (SVM, Radial Basis Function -RBF, Random Forest Transform-RFT, and KNN) are used with the supervised procedure. The testing was performed on multispectral palmprint databases. Cheng et al. [29]  proposed Deep Convolutional Features-Based Supervised Hashing (DCFSH).
They used CNN-F architecture to extract the palmprint convolutional features, followed by learning binary coding from distilled deep features. DCFSH is evaluated on a multispectral palmprint database. The Hamming distance is employed in the matching steps. Zhong et al. [30]  proposed a new method to achieve end-to-end palmprint recognition using Siamese network. In their network, two parameter-sharing Visual Geometry Group-16 (VGG-16) networks were used to extract convolutional features of two input palmprints images, and the top network directly got the similarity of two input palmprints based on their convolutional features. Bensid et al. [19]  proposed a simple new deep learning feature extraction algorithm for an efficient multispectral palmprint identification system called Discrete Cosine Transform Network (DCTNet).
Genovese et al. [17]  proposed PalmNet, which is a convolutional network that uses Gabor responses and PCA filters through an unsupervised procedure applied on different touchless palmprint databases and uses the 1-NN classifier based on the Euclidean distance for classification step. Besides, Zhao et al. [33]  proposed a joint constrained least-square regression (JCLSR) model with deep convolutional neural networks to solve the under-sampling classification problem by extracting different deep local convolution features using different patches of the same palmprint image. The experiments of the proposed method (JCLSR) are performed on touchless and multispectral palmprint databases. Table  2  include a summary of deep learning methods for palmprint recognition. Fei et al. [31]  proposed LRRIPLD which is a new Low-Rank Representation (LRR) model integrated with principal line distance for contactless palmprint recognition.
LRRIPLD generates a graph that is more distinct than LRR because main line distances effectively improve clustering results by increasing the weights of the links between similar samples. The approach is tested on three palmprint databases IITD-Touchless, GPDS-Touchless, and CASIA. Arora et al. [32]  introduced PalmHashNet, a novel indexing method that learns compact feature vectors for palmprint identification. They used the Softmax loss function with additive margin to train the model to index the palmprint database and to simultaneously learn the feature vector embeddings. Furthermore, to generate an index table, the learned embeddings are indexed using the k-means clustering and locality sensitive hashing techniques. PalmHashNet is evaluated on four publicly available palmprint databases CASIA, IITD-Touchless, Tongji-Contactless, and PolyU II.
Features extraction Feature extraction is a key module for recognition systems. The acquired biometric data are processed, and only the salient information is extracted to form a new representation of the data. Ideally, for each person, this new representation should be unique. In our scheme, to extract highly discriminative palmprint features, PalmNet deep learning is used to extract the features vector of each data type. It is a particular case of an image classification deep learning baseline, which consists of three stages: (1) convolutional stage, (2) binarization stage, (3) histograms stage. Thus, the block diagram of PalmNet algorithm is presented in Fig. 3  and can be summarized as follows  [17] : As shown in Fig. 3 , the convolution process is ensured by two layers of the Gabor filter bank: -First convolutional layer (L 1 ) consisting of k 1 filters.
Experiments and results This section presents the experimental evaluation, by conducting experiments on four popular and publicly available databases which are CASIA multispectral palmprint, PolyU multispectral palmprint, Tongji contactless palmprint, and PolyU 2D/3D contactless palmprint. First, we give a brief description of the adopted palmprint databases (Sect. 4. 1). Second, we present the setup of our approach in the experimental setup (Sect. 4. 2). Finally, in the experimental results (Sect. 4. 3), we discuss and analyse the results.
CASIA multispectral palmprint database V1. 0 The CASIA Multispectral Palmprint Image Database comprises 7,200 palm images obtained from 100 different individuals using self-designed multiple spectral imaging devices described in  [46]. In this database, the images of each hand are collected in two separate sessions. The time interval between two sessions is more than one month. In each session, there are three samples. Each sample includes six palm images, which are captured at the same time with six different electromagnetic spectrums. Wavelengths of the illuminator corresponding to the six spectrums are 460, 630, 700, 850, 940 nm, and white light, respectively (see  Fig 4). Between two samples, certain degrees of variations of hand postures are allowed. All palm images are low resolution \150dpi stored as 8-bit gray-level images per band with dimensions of 128 Â 128.
Tongji contactless palmprint dataset Tongji Contactless Palmprint Dataset comprises 12,000 images obtained from 600 different palms using the proprietary touchless acquisition device described in  [48]. Tongji University collected images from 300 volunteers, including 192 males and 108 females. Among them were 235 people between the ages of 20 and 30 and the others between the ages of 30 and 50. The left and right samples were collected in two separate sessions (see  Fig 6). In each session, the person provides ten images per palm. Therefore, 40 images from 2 palms were collected from each person. The average period of time between the first and second sessions was approximately 61 days. The minimum and maximum time intervals were 21 days and 106 days, respectively. All palm images are stored as 8 À bit graylevel images per band with dimensions of 128 Â 128.
Experimental setup The identification system can operate in two modes: openset and closed-set identification. In the first mode, the person to identify is not guaranteed to exist in the database, but is assumed to exist in the second mode. The proposed approach has been tested in both modes of our work. The three principal criteria illustrate the performance of a biometric system for open-set identification: -False Rejection Rate or FRR: It reflects the percentage of persons required to be accepted, but the system rejects them. It is described by the following equation:  We can use another performance measurement, which is the Genuine Acceptance Rate (GAR). It represents the system identification rate. This rate is defined as the percentage of genuine users accepted by the system, which is expressed as follows: GAR ð%Þ ¼ 100 À FRR ð%Þ ð 18Þ So, we can display the Receiver Operating Characteristics (ROC) curves, which are the GAR against the FAR or the FRR against the FAR  [50].
In closed-set identification, to measure the accuracy performance of a biometric system, we use the Cumulative Matching characteristic (CMC) curve. It shows the ranking of individual templates based on the match rate. This curve is associated with two criteria Rank of Perfect Rate (RPR), defined as at which rank the identification rate attempts 100% and Rank-One Recognition (ROR) defined as the percentage of persons recognized by the system as a function of a variable ''rank''. Moreover, to evaluate the computational time requirements of each algorithm, the performance indicator time refers to the CPU time needed to classify one palmprint image in seconds. To evaluate the efficiency of our proposed method, the experiments were conducted on two sub-datasets. The first sub-dataset is used for the training phase, while the second is for the testing phase.
Each sub-dataset contained 50% of the images in the database. To reach the best possible recognition accuracy on the considered datasets, we experimentally tuned some filter parameters, and we selected the others by considering the optimal values found in the literature  [17]. For the Gabor filters, the chosen values of the filter numbers k 1 and k 2 were k 1 ¼ k 2 ¼ 13 in two stages. The fixed-scale 2 À D Gabor filters F is 10 with dimensions h 1 ¼ h 2 ¼ 31 and the adaptive multiscale 2 À D Gabor filters A 0 is 3. The feature vector size is computed by using the Eq. 7 as follows: H j j ¼ k 1 n B 2 k 2 ¼ 13:4:2 13.
All the computation times presented in this paper are obtained with MATLABr 2018a in PC with a processor (Intel Core i7-4710MQ) 2. 50-GHz and RAM 16 GB. The experimental results can be divided into three subparts: the first subpart includes the results obtained from the unimodal identification system evaluated on the CASIA and PolyU multispectral databases, and the contactless Tongji and PolyU 2D/3D databases. While in the second subpart, the results of the multimodal identification system are presented. As for the third subpart, we do a comparison study with some works in literature.
CASIA multispectral database results In order to evaluate and test the effectiveness of the proposed method, experiments are done on the CASIA multispectral database with 100 persons and six spectral bands (460, 630, 700, 850, 940 nm, and white light). Since our evaluation adopts random splits for training and testing, we studied the effect of the amount of training on the method's performance. To this end, we run our method using three different random trainingtesting splits (without Feature selection) (see Table  4 ). From the obtained results, we can observe that the 50% training-testing split gives the best results for all spectral bands. Therefore, the experiments will be conducted using the 50% training-testing splits. Also, to evaluate the performance of the proposed approach, we calculated the performance indicators for several random splits of the training and testing images.
Thus, we calculated the standard deviation of the main performance indicators EER and ROR in two cases: without and with FS and dimensionality reduction using the SVM classifier. To this end, we have adopted ten random image splits, each of which contains six images for training and the remaining six images for testing. The number of genuine and impostor comparisons for each spectral band is 600 and 29,700, respectively. Table  5  shows the different results of ERR (%) and ROR (%) on the ten random splits for two cases without feature selection (FS) and with FS (Fisher score) and dimensionality reduction (WPCA) of six spectral bands from the CASIA database. This table also shows the mean and standard deviation of ERR (%) and ROR (%). Comparing all the results in the two tables shows that feature selection and dimensionality reduction have improved average performance (i.
2. PolyU multispectral database results In the following, we present experimental results of the proposed system evaluated on the PolyU multispectral database, which contains 500 persons and various modalities (Red, Green, Blue, and NIR). In our experiment, six images of each person are selected for training and the other six for testing. Namely, 3000 images are used for training, and 3000 images are used for testing for each modality. Furthermore, there are 3000 genuine comparisons, and 748,500 impostor comparisons are generated for each band. Thus, in order to see the performance of the biometric system with two modes of identification and CPU time needed to classify one palmprint image, we present the findings in Table  7. We can observe from the results obtained by the proposed system that NIR and Blue spectral bands presented the best results in terms of the EER(%) and ROR(%) values.
For example, the results of the open-set identification with 1-NN classifier and without FS give EERs equal to 4:008 Â 10 À4 % and 8:016 Â 10 À4 % for NIR and Blue spectral bands, respectively. While, for the closed-set identification,  In our case, we do not need the fusion for PolyU and Tongji databases because we got perfect results in the unimodal system (EER = 0:000% and ROR = 100%).
PolyU 2D/3D contactless database results In order to evaluate the efficiency of the proposed method in the contactless database, we utilized the PolyU 2D/3D database, which contains 400 people. In our experiment, we used 2D images from this database splitting each person's 20 images into ten for training and ten for testing. Each sample is tested using 4000 images for training and 4000 images for testing. As a result, there are 4000 genuine comparisons and 798,000 impostor comparisons. Table  9  shows the performance of our unimodal biometric system with two modes of identification system (open-set and closed-set) and the CPU time needed to classify one palmprint image. From the obtained results, we observe that the SVM classifier achieves better performance than the NN classifier, especially in the case of the Fisher score.
Matching score level fusion is the most widely used in the biometric system. In our work, we fuse only the spectral bands of CASIA database palmprint at the matching score level to improve system performance. The other databases have given perfect results with unimodal biometric systems (EER = 0:000% and ROR = 100%). The experiment was conducted with four methods of fusion which are the SUM rule (SUM), the product rule (MUL), and their weighted versions, that is, weighted SUM rule (WHT SUM) and weighted product rule (WHT MUL). The performance of our multimodal identification system is shown in Tables  10  and 11. The analysis of data showed that the results of the multimodal fusion were much better than those of the unimodal biometric systems. As can be seen from the results, the lowest EER of multimodal identification was obtained by using the combination of all spectral bands that are always better than the lowest results of the unimodal system.
Comparative study To prove the effectiveness of the proposed approach against alternative methods, we did a comparative study along with some works found in the literature. Thus, in this paper, we provided unimodal and multimodal identification systems using multispectral and contactless palmprint images. The results validated the robustness and effectiveness of the proposed method. The system reached very high identification accuracy (EER ¼ 0:000% and ROR ¼ 100%). Therefore, a comparison of some related and existing techniques must be made on the same databases. For the multimodal biometric system, a comparative study was performed in Table  13  with the works that used fusion at the matching score level and 50% train-test split procedure. It is observed that the proposed algorithm has also given perfect identification performance (EER ¼ 0:000%, ROR ¼ 100%) like the two other works, but it has reduced the features vector to 410 instead of 46,060 for the two other works.
The proposed approach was evaluated using four popular and publicly available palmprint databases. The extensive experiments presented in this work have validated the robustness and effectiveness of our method by achieving high recognition accuracy with a significantly smaller number of features. The unimodal identification system performed on multispectral PolyU database and contactless Tongji and PolyU 2D/3D databases obtained a very high identification accuracy (EER ¼ 0:000% and ROR ¼ 100%). It gave a CPU time less than 0. 009 s and reduced the feature vector size to 410. Likewise, the multimodal identification system performed on the CASIA database offers perfect results EER ¼ 0:000% for the openset identification and ROR ¼ 100% for the closed-set identification. In the future, we will test our proposed method with other large databases, such as medical images.
PolyU multispectral palmprint images database PolyU Multispectral Palmprint Images Database comprises 6000 images obtained from 500 different palms for each band using palmprint images capturing device designed by Hong Kong Polytechnic University researchers described Neural Computing and Applications 123 in [47]. The multispectral database contains cropped multispectral palmprint images of four different bands (Red, Green, Blue, and NIR) are shown in Fig 5. The images were collected in two separate sessions at a time interval of about two months. In each session, the person provides 6 images per palm, so there are 12 images for each person. Therefore, 48 spectrum images of all illumination from 2 palms were collected from each person. The average time interval between the first and the second sessions was about nine days.
PolyU 2D/3D contactless palmprint dataset The PolyU 2D/3D Contactless Palmprint database contains 8000 images collected from 400 palms of 200 volunteers. The Bio-Research Center (UGC/CRC) of Hong Kong Polytechnic University [49] created the PolyU 2D/3D database. The participants' gender includes 136 males and 64 females with the age range of 18 to 50 years. Each person provided twenty samples for both the left and right palms. The left and right palms from the same person can be considered as belonging to different classes. Thus, there are 400 classes of 2D/3D palmprint image samples. The samples have been collected in two sessions, where ten samples are captured in each session, and the average time between the two sessions is one month. All 2D images in this database are stored as 8 À bit gray-level images with dimensions of 128 Â 128.
The computing time represents the CPU time needed to classify one palmprint image. To obtain an equitable comparison, we chose works where the percentage of training and testing is 50%. From this table, we observe that the proposed algorithm (Simplified Palm-Net-Gabor) provides high identification performance for multispectral PolyU database and contactless Tongji and PolyU 2D/3D databases, using a substantially lower number of features compared with other methods listed in the same table. On the other hand, for the multispectral CASIA database, a fusion process is used to improve the identification performance.
4. 1. 2 12 PolyU multispectral palmprint images database PolyU Multispectral Palmprint Images Database comprises 6000 images obtained from 500 different palms for each band using palmprint images capturing device designed by Hong Kong Polytechnic University researchers described Neural Computing and Applications 123 in [47]. The multispectral database contains cropped multispectral palmprint images of four different bands (Red, Green, Blue, and NIR) are shown in Fig 5. The images were collected in two separate sessions at a time interval of about two months. In each session, the person provides 6 images per palm, so there are 12 images for each person. Therefore, 48 spectrum images of all illumination from 2 palms were collected from each person. The average time interval between the first and the second sessions was about nine days.
4. 1. 4 14 PolyU 2D/3D contactless palmprint dataset The PolyU 2D/3D Contactless Palmprint database contains 8000 images collected from 400 palms of 200 volunteers. The Bio-Research Center (UGC/CRC) of Hong Kong Polytechnic University [49] created the PolyU 2D/3D database. The participants' gender includes 136 males and 64 females with the age range of 18 to 50 years. Each person provided twenty samples for both the left and right palms. The left and right palms from the same person can be considered as belonging to different classes. Thus, there are 400 classes of 2D/3D palmprint image samples. The samples have been collected in two sessions, where ten samples are captured in each session, and the average time between the two sessions is one month. All 2D images in this database are stored as 8 À bit gray-level images with dimensions of 128 Â 128.
[29] 2017 DCFSH PolyU 193 7752 0. 000 - - PolyU 500 6000 0. 281 - Zhong et al. [30] 2018 Siamese network XJTV 114 2078 4. 559 - PolyU 500 6000 0. 000 100 CASIA M 100 1200 0. 111 99. 33 25/75 PolyU 500 6000 0. 000 100 Bensid et al. [19] 2018 DCTNet with Two-stages CASIA 100 1200 0. 003 99. 83 50/50 CASIA T 624 5455 0. 720 99. 77 IITD T 467 2669 0. 520 99. 37 REST T 358 1937 4. 500 97. 16 Genovese et al. [17] 2019 PalmNet-GaborPCA Tongji T 600 5182 0. 160 99. 83 50/50 CASIA 312 2750 - 99. 84 PolyU 500 6000 - 100 Zhao et al. [33] 2020 JCLSR IITD T 230 2601 - 98. 17 70/30 CASIA 312 2496 0. 031 99. 98 ITTD T 460 1841 0. 390 99. 62 Tongji T 193 6000 0. 530 97. 85 Arora et al. [32] 2021 PalmHashNet PolyU 300 3860 0. 011 99. 83 - II -N. ind = Number of individuals -N. samp = Number of samples -M= Multispectral database -T= Touchless database.