Face recognition using eigenface with naive Bayes

Putranto, Ega Bima; Situmorang, Poldo Andreas; Girsang, Abba Suganda

doi:10.1109/kicss.2016.7951418

Cited by 15 publications

(11 citation statements)

References 7 publications

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“…Dalam penelitian ini menggunakan Naïve Bayes untuk mengklasifikasi hasil ekstraksi fitur bentuk untuk mengenali objek bunga. Pada penelitian yang dilakukan oleh Bima, dkk menggunakan Naïve Bayes Classifier dengan Eigenface untuk memprediksi tingkat akurasi pengenalan wajah [11], di dalam penelitiannya berhasil meningkatkan tingkat akurasi menjadi 89.5%. sedangkan untuk sistem prediksi menggunakan Naïve Bayes pada penelitian Azman didapat tingkat akurasi sebesar 83.83% [12] Naïve Bayes Classifier merupakan salah satu metode klasifikasi yang dapat mengenali objek dengan dataset minimum [13].…”

Section: Pendahuluanunclassified

Pengenalan Citra Bunga Menggunakan Segmentasi Otsu Treshold dan Naïve Bayes

Rosyani¹,

Hariansyah

2020

jsi

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Bunga merupakan salah satu bagian tumbuhan yang mempunyai warna mencolok dibanding batang kayu dan daun. Bunga merupakan salah satu fitur penting dalam proses pengenalan objek. Proses pengenalan objek pada segmentasi digital sangat penting untuk memisahkan antara background dan foreground dari sebuah citra. Hal ini bertujuan untuk mendapatkan fitur-fitur yang dibutuhkan. Citra bunga pada penelitian ini memiliki kompleksitas gambar cukup sulit karena ada daun dan batang pohon di sekeliling gambar bunga tersebut. Oleh Karena itu, dalam penelitian ini segmentasi yang diusulkan menggunakan Otsu Treshold sebagai metode untuk memisahkan foreground dan background. Proses segmentasi sangat menentukan untuk mendapatkan fitur bentuk berupa area, eccentricity, dan perimeter. Hasil kalkulasi dari fitur-fitur tersebut akan diklasifikasi menggunakan algoritma Naïve Bayes dengan menggunakan 120 citra bunga dari dataset 17 flower. Dataset tersebut akan dibagi menjadi data tes dan data training, dan menggunakan cross validasi (k=10). Hasil dari klasifikasi menggunakan Naïve Bayes mendapatkan tingkat akurasi yang lebih tinggi sebesar 99.168% dengan relative absolute error sebesar 8.0937% dibandingkan dengan penelitian sebelumnya sebesar 83.83%.

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Section: Pendahuluanunclassified

Pengenalan Citra Bunga Menggunakan Segmentasi Otsu Treshold dan Naïve Bayes

Rosyani¹,

Hariansyah

2020

jsi

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“…2. Naive Bayes classifier a probabilistic approach that is simple but effective for face recognition problems [1]. This classifier has been used in various applications like image recognition [12], image retrieval [11] and natural language processing.…”

Section: Fig2: Face Recognition Process [2]mentioning

confidence: 99%

“…Naive Bayes classifier expect the occurrence of a specific feature is distinct from the occurrence of other feature. It learns from data as well as predict class with probability associated with these class [1]. Naive Bayes classifier as depicted in equation (1).…”

Section: B Naive Bayes Classifiermentioning

confidence: 99%

“…Use of human face for security purpose, the face recognition technology has presented a sufficiently a good amount of work in last few years. There are a few techniques for biometrics recognition, for example, iris recognition, fingerprints and face recognition [1]. In comparisons with other biometric systems, the face recognition has an advantage because the images of the face can be captured non-intrusively.…”

Section: Introductionmentioning

confidence: 99%

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Face Recognition using Probabilistic Model for Locally Changed Face

Khedgaonkar¹,

Singh²,

Raghuwanshi³

et al. 2019

IJITEE

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Face recognition is an attention-grabbing area in research field due to various challenges like aging, pose variation, facial expression, and illumination problem. Now-a-days, plastic surgery is a standout amongst the above mentioned exciting issues of face recognition. Local plastic surgery is a type of plastic surgery in which any one feature of the face is changed instead of all features of face. In this paper, the face recognition on local plastic surgical faces using probabilistic approach is presented, where a probabilistic approach like Naive Bayes Classifier, Neural Network Classifier are used to recognize the faces with local plastic surgery from the database. Naive Bayes classifier is fused with Expectation Maximization Algorithm (EMA) for better recognition of the faces from the database. Finally, Results of Naive Bayes Classifier, Naive Bayes Classifier with EMA is evaluated on standard Plastic Surgery Database(PSD). Similarly, Neural network classifier is also been tested on PSD database, which will aid to decide which classifier is efficient for recognizing plastic surgical faces. The motive of this paper is give increase in recognition rate with the help of effective classifier.

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“…It is a family of classifiers based on the Bayes theorem (2) with an assumption of independence between the predictors, having its use in several types of classifications, such as face recognition (PUTRANTO; SI-TUMORANG; GIRSANG, 2016;CHEN et al, 2016) and objects(ZHANG; LIANG, 2016), reading emotions in songs (AN; SUN; WANG, 2017), texts(OGUL; OZCAN; HAKDAGLı, 2017). In simple terms, Naive Bayes are probabilistic classifiers in which it is assumed that the presence of a particular characteristic in a class is not related to the presence of any other resource (VIDHYA, 2015).…”

Section: Naive Bayesmentioning

confidence: 99%

Embedded Linux System for Digital Image Recognition using Internet of Things

Freire

Pereira

Silva

et al. 2018

jme

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The present paper describes the use of Digital Image Processing and Internet of Things for gestures recognition using depth sensors available on Kinect R device. Using the open source libraries OpenCV and libfreenect, image data are translated and used for communicating Raspberry Pi embedded Linux system with PIC microcontroller board for peripheral devices controlling. LED is triggered according to the hand gesture representing the corresponding number. Data are stored in a PHP Apache server running locally on Raspberry Pi. The proposed system can be used as a multifunctional tool in areas such as learning process, post-traumatic rehabilitation and visual and motor cognition time. Using image binarization and Naive-Bayes classifier, the achieved results show error lower than 5%.

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Face recognition using eigenface with naive Bayes

Cited by 15 publications

References 7 publications

Pengenalan Citra Bunga Menggunakan Segmentasi Otsu Treshold dan Naïve Bayes

Pengenalan Citra Bunga Menggunakan Segmentasi Otsu Treshold dan Naïve Bayes

Face Recognition using Probabilistic Model for Locally Changed Face

Embedded Linux System for Digital Image Recognition using Internet of Things

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