Palm print recognition based on harmony search algorithm

Mustafa, Ali; Chyad, Haitham Salman; Haleot, Rafid A.

doi:10.11591/ijece.v11i5.pp4113-4124

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…• The correct recognition rate (CRR) is the percentage of correctly recognized samples among all samples examined according to equation ( 5) [23].…”

Section: Resultsmentioning

confidence: 99%

Car Logo Image Extraction and Recognition using K-Medoids, Daubechies Wavelets, and DCT Transforms

Rajab,

George

2024

Iraqi Journal of Science

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Recognizing cars is a highly difficult task due to the wide variety in the appearance of cars from the same car manufacturer. Therefore, the car logo is the most prominent indicator of the car manufacturer. The captured logo image suffers from several problems, such as a complex background, differences in size and shape, the appearance of noise, and lighting circumstances. To solve these problems, this paper presents an effective technique for extracting and recognizing a logo that identifies a car. Our proposed method includes four stages: First, we apply the k-medoids clustering method to extract the logo and remove the background and noise. Secondly, the logo image is converted to grayscale and also converted to a binary image using Otsu's method. Thirdly, the Daubechies wavelet with DCT transforms is applied to extract a feature vector for each image. Finally, the Canberra distance is used to match the tested image's feature vector to all feature vectors in the database. The test results indicate the highest CRR, accuracy, and precision at 99.37%, 99.39%, and 99.80%, respectively. This system is applicable to intelligent surveillance systems.

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“…• The correct recognition rate (CRR) is the percentage of correctly recognized samples among all samples examined according to equation ( 5) [23].…”

Section: Resultsmentioning

confidence: 99%

Car Logo Image Extraction and Recognition using K-Medoids, Daubechies Wavelets, and DCT Transforms

Rajab,

George

2024

Iraqi Journal of Science

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“…In 2021 Mustafa et al presented a new method for identifying palm print using the Gaussian distribution calculation method based on the harmony search algorithm (HSA), where the proposed method consists of three main stages (training, testing, and recognition), which are trained through two stages: The first stage is the initial processing, which consists of many sub-stages (hash, ROI, and edge detector) and the second stage is extracting features from palm print images. Through the use of the Consistent Search Algorithm (HSA), this proposed system was tested on the standard dataset PolyU for palm print images and the results showed a classification accuracy of (92.60%) [17].…”

Section: Review Of Literaturementioning

confidence: 99%

Palmprint Identification Using Dolphin Optimization

Al-Taie,

Khaleel

2024

Period. Polytech. Elec. Eng. Comp. Sci.

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Palm print recognition is a rapidly evolving area in the field of biometrics, providing a high level of security for various applications Advances in scanning technology and software have led to faster and more accurate palm print analysis, we have proposed in this paper a system for palm recognition using the Dolphin Optimization Algorithm (DOA) as a computational technique inspired by nature aimed at solving complex improvement problems. We reduced the number of image features using the Histograms of oriented gradients (HOG) algorithm, we named this method as (DOA) and we also proposed a hybrid method by integrating the DOA algorithm with the Support Vector Machine (SVM) model to improve prediction accuracy by combining DSA's ability to search for global optimal solutions with the effective classification capabilities of SVM, this allows The hybrid approach creates a robust and the proposed hybrid method was named (SVM-DOA), and we also proposed a hybrid method by integrating the DOA algorithm with fuzzy c – mean (FCM) and we named the proposed hybrid method (DOA-fuzzy membership), we verified the validity of the proposed method on public database images of palm print. experiment show that the average accuracy rate of the dolphin swarm algorithm (DSO) is (96.8%), while the average accuracy rate of the proposed hybrid algorithm (DSO-SVM) is (97.8%), and the average accuracy of the proposed hybrid algorithm (DSO-fuzzy membership) is (98.1%).

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“…The results of the evaluation of the earprint recognition scheme were evaluated utilizing two measures: the recognition rate (RR) and false alarm rate (FAR). The ( 4) and ( 5) were used in order to calculate those two measurements [35]- [39]. The best recognition rate was achieved, as shown in Table 4.…”

Section: Recognition Phasementioning

confidence: 99%

Human ear print recognition based on fusion of difference theoretic texture and gradient direction pattern features

Saleh

Mustafa²,

Chyad³

2023

IJEECS

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<span lang="EN-US">Human ear recognition can be defined as a branch of biometrics that uses images of the ears to identify people. This paper provides a new ear print recognition approach depending on the combination of gradient direction pattern (GDP2) and difference theoretic texture features (DTTF) features. The region of interest (ROI), the gray scale of the ear print was cut off, noise removal by the median filter, histogram equalization, and local normalization (LN) are the first steps in this approach. After the image has been processed, it is used as input for the fusion of GDP2 and DTTF for extracting the features of ear print images. Lastly, the Gaussian distribution (GD) was utilized to compute the distance among fusion feature vectors (FV) for ear print images for recognizing ear print images for people using a set of images that had been trained and tested. The unconstrained ear recognition challenge (UERC) database, which comprises 330 subjects for ear print images, provides the approach that was suggested by employing ear print databases. Furthermore, experimental results on images from a benchmark dataset reveal that statistical-rely super-resolution methods outperform other algorithms in ear recognition accuracy, which was around 93.70% in this case.</span>

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Palm print recognition based on harmony search algorithm

Cited by 6 publications

References 12 publications

Car Logo Image Extraction and Recognition using K-Medoids, Daubechies Wavelets, and DCT Transforms

Car Logo Image Extraction and Recognition using K-Medoids, Daubechies Wavelets, and DCT Transforms

Palmprint Identification Using Dolphin Optimization

Human ear print recognition based on fusion of difference theoretic texture and gradient direction pattern features

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