Paralel Spatial Pyramid Convolutional Neural Network untuk Verifikasi Kekerabatan berbasis Citra Wajah

Rachmadi, Reza Fuad; Purnama, I Ketut Eddy

doi:10.14710/jtsiskom.6.4.2018.152-157

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…Analisis dan pemodelan citra wajah merupakan bidang riset yang aktif dalam era big data saat ini. Beragam metode telah digunakan dalam penelitian citra wajah, di antaranya metode klasifier parallel spatial pyramid Convolutional Neural Network (CNN) untuk verifikasi kekerabatan berbasis citra wajah [4] dan identifikasi citra wajah dengan Hidden Markov Model (HMM) [5]. Aplikasi pengenalan citra sketsa wajah manusia juga telah dikembangkan dengan beberapa metode lain, di antaranya Structure Co-occurrence Texture (SCOOT) untuk evaluasi struktur spasial dan ko-okurensi tekstur [6], klasifier Bayesian [1], dan Eigenface [7].…”

Section: Pendahuluanunclassified

“…Di sisi lain, sketsa yang dilukis dengan pensil di atas kertas (viewed sketches) memiliki tekstur yang berbeda dibandingkan dengan kulit manusia yang diambil pada foto, terlebih dengan adanya tekstur bayangan untuk menyampaikan informasi bayangan 3D [7]. Berbeda dengan [1], [4]- [9] yang menggunakan data citra foto sebagai masukan, penelitian ini mengkaji aplikasi pengenalan wajah menggunakan dokumen hasil sketsa wajah (viewed sketches) sebagai masukan sistem dan dibandingkan dengan basis data foto wajah. Berbeda dengan [10] yang menggunakan LFDA, ekstraksi ciri dari sketsa dan foto wajah dalam kajian ini dilakukan dengan metode PCA.…”

Section: Pendahuluanunclassified

“…Seperti halnya [9], hasil penelitian ini telah berhasil mengidentifikasi citra sketsa yang berbeda dengan foto wajah, terutama pada data fitur warna, tekstur, dan proyeksi gambar. Walaupun demikian, identifikasi citra sketsa ini mempunyai akurasi yang lebih tinggi daripada aplikasi verifikasi kekerabatan dari foto wajah dengan CNN sebesar 66 % dalam [4], identifikasi dari foto wajah dengan HMM sebesar 77,7 % dalam [5], dan pengenalan foto wajah menggunakan PCA dan jaringan saraf sebesar 82,81 % dalam [8]. Selain itu, akurasi pengenalan citra sketsa menggunakan PCA dalam aplikasi ini sebanding dengan [1] yang menggunakan klasifier Bayesian dengan akurasi sebesar 97 %, namun lebih tinggi daripada aplikasi dalam [10] yang menggunakan metode LFDA sebesar 55-57 %, dalam [6] yang menggunakan SCOOT sebesar 78,8 %, dan dalam [7] yang menggunakan eigenface sebesar 73,27%.…”

Section: B Pengenalan Sketsa Modifikasi Dan Data Sketsa Secara Langsungunclassified

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Face sketch recognition using principal component analysis for forensics application

Purwandari

Erlansari

Wijanarko

et al. 2020

Jurnal Teknologi dan Sistem Komputer

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Recognition of human faces in forensics applications can be identified through the Sketch recognition method by matching sketches and photos. The system gives five criminal candidates who have similarities to the sketch given. This study aims to perform facial recognition on photographs and sketches using Principal Component Analysis (PCA) as feature extraction and Euclidean distance as a calculation of the distance of test images to training images. The PCA method was used to recognize facial images from pencil sketch drawings. The system dataset is in the form of photos and sketches in the CUHK Face Sketch database consists of 93 photos and 93 sketches, and personal documentation consists of five photos and five sketches. The sketch matching application to training data produces an accuracy of 76.14 %, precision of 91.04 %, and recall of 80.26 %, while testing with sketch modifications produces accuracy and recall of 95 % and precision of 100 %.

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Section: Pendahuluanunclassified

Section: B Pengenalan Sketsa Modifikasi Dan Data Sketsa Secara Langsungunclassified

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Face sketch recognition using principal component analysis for forensics application

Purwandari

Erlansari

Wijanarko

et al. 2020

Jurnal Teknologi dan Sistem Komputer

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“…Apart from these traditional methods, deep learning-based approaches lately achieved state-of-the-art performance in kinship verification. Widely used architectures, e.g., VGG16 [9], GAN & ResNet [10], SphereFace [11], SPCNN [12], ShallowResNet [13] and VGGFace [14]- [16] have been used for this task. Laiadi et.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Task Comparator Framework for Kinship Verification

Hörmann¹,

Knoche²,

Rigoll³

2020

Preprint

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Approaches for kinship verification often rely on cosine distances between face identification features. However, due to gender bias inherent in these features, it is hard to reliably predict whether two opposite-gender pairs are related. Instead of fine tuning the feature extractor network on kinship verification, we propose a comparator network to cope with this bias. After concatenating both features, cascaded local expert networks extract the information most relevant for their corresponding kinship relation. We demonstrate that our framework is robust against this gender bias and achieves comparable results on two tracks of the RFIW Challenge 2020. Moreover, we show how our framework can be further extended to handle partially known or unknown kinship relations.

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“…For instance, studies on ripeness estimation of grapes [22], banana [23], pineapple [24], durian [25], coffee beans [26], and other artificially ripened fruits [27] have utilized the potential of CNN in carrying out high accuracy classification. Other useful studies on the manipulation of CNN modeling are shown in other applications like in [28]- [30], while commonly used machine learning like SVM, Decision Tree, and Naïve Bayes can also be used in training the data [31], [32].…”

Section: Introductionmentioning

confidence: 99%

Maturity classification of cacao through spectrogram and convolutional neural network

Bueno

Valenzuela

Arboleda

2020

Jurnal Teknologi dan Sistem Komputer

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Cacao pod's ideal harvesting time is when it is about to be ripe. Immature harvest would result in hard cacao beans not suitable for fermentation, while overripe cacao pods lead to fungal-infected, defective, and poor-quality yields. The demand for high-quality cacao products is expected to rise due to advancing technology in the present. Pre-harvesting needs to provide optimal identification of which amongst the pods are ripened enough and ready for the next stage of the cacao process. This paper recommends a technique to determine the ripeness of cacao. Nine hundred thirty-three cacao samples were used to collect thumping audio data at five different pod's exocarp locations. Each sound file is 1 second long, creating 4665 cacao sound file datasets at 16kHz sample rate and 16-bit audio bit depth. The process of the Mel-Frequency Cepstral Coefficient Spectogram was then applied to extract recognizable features for the training process. The deep learning method integrated was a convolutional neural network (CNN) to classify the cacao sound successfully. The experimental design model's output exhibits an accuracy of 97.50 % for the training data and 97.13 % for the validation data. While the overall accuracy mean of the classification system is 97.46 %, whether the cacao is unripe or ripe.

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Paralel Spatial Pyramid Convolutional Neural Network untuk Verifikasi Kekerabatan berbasis Citra Wajah

Cited by 11 publications

References 10 publications

Face sketch recognition using principal component analysis for forensics application

Face sketch recognition using principal component analysis for forensics application

A Multi-Task Comparator Framework for Kinship Verification

Maturity classification of cacao through spectrogram and convolutional neural network

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