Image Segmentation for Skin Detection

Mohammed, Sajaa G.; Majeed, Abdulrahman H.; Aldujaili, Ali Abdulameer; Hassan, Enas Kh.; Abdul-Jabbar, Safa S.

doi:10.35741/issn.0258-2724.55.1.17

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…Mohammed et al [25] Differentiate the pixels as skin and non-skin based on a metric that measures the distance of pixel colors to skin tones. In this work, the author proves that the YCbCr color space can give better performance than the RGB image due to the isolation of the illuminance channel.…”

Section: Related Workmentioning

confidence: 99%

Human Skin Detection and Segmentation Based on Convolutional Neural Networks

Dhayea,

El Abbadi,

Abdul Hasan

2024

Iraqi Journal of Science

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Human skin detection is the process of classifying the image pixels or regions as skin or non-skin. Skin detection has many applications, such as face tracking, skin diseases, nudity recognition, hand gestures, video surveillance, web content filtering, and pornographic content filtering. Skin detection is a challenging problem due to skin color variation, the human race, aging, gender, makeup, complex backgrounds, etc. This paper suggests detecting the skin region in the image and finding the location of the skin based on a convolutional neural network. In this proposal, the proposed CNN will be modified by adding two layers before the first layer of CNN and after the last layer of CNN. The main purpose of these layers is to prepare the input image by using a sliding window, which inputs an indexed small part of the image into the CNN. The network classifies each part as skin or non-skin and then sends the result into the second suggested layer. After processing all the image pixels, the non-skin blocks are mapped to the original image as black regions. The final image contains the skin regions with black in the background. The contribution of the proposed method is the ability to detect the skin from any part of the human body, unlike previous works, which focused on one part of the body. The input image is processed as blocks instead of the entire image as in the previous works, and then in the output, the original image is reconstructed. This method works well with most of the challenges that face the detection of skin, and finally, the designed network facilitates the localization and segmentation of the skin region almost accurately, while the previous networks focused on the classification of the image as skin or non-skin. The accuracy of the detection of skin when testing with images different from the training images was 95.4%.

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Section: Related Workmentioning

confidence: 99%

Human Skin Detection and Segmentation Based on Convolutional Neural Networks

Dhayea,

El Abbadi,

Abdul Hasan

2024

Iraqi Journal of Science

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“…Dalam pemrosesan ini akan dilakukan beberapa tahap meliputi tahap pertama dengan memilih citra yang diinginkan kemudian citra akan ditampilkan, dari citra ini kemudian dilakukan pemrosesan lagi meliputi tahap konversi nilai RGB ke YCbCr dan akhirnya akan diperoleh hasil skin color detection. Penggunaan YcbCr karena ruang warna YCbCr melakukan deteksi piksel kulit yang lebih baik daripada gambar RGB [14].…”

Section: Hasil Dan Pembahasanunclassified

DETEKSI CITRA PORNOGRAFI MENGGUNAKAN SKIN COLOR DETECTION DAN EIGENPORN BERDASARKAN RUANG WARNA YCbCr

Liantoni

Wahyudi

2022

Mnemonic

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Penggunaan pornografi adalah pengalaman gender yang dimulai pada masa remaja awal. Meskipun faktor penggunaan pornografi bervariasi di seluruh konteks sosial, paparan pornografi telah menjadi bagian normatif dari perkembangan seksualitas remaja. Dengan menjamurnya gambar-gambar porno di Internet, penting untuk mendeteksi gambar-gambar porno secara otomatis dengan menganalisis konten gambar. Sebagian besar sistem deteksi tradisional didasarkan pada fitur tingkat rendah dan menghasilkan banyak kesalahan positif karena gambar yang mengandung wilayah besar dengan warna seperti kulit. Pada penelitian ini, dilakukan deteksi citra pornografi menggunakan skin color detection yang ditambahkan eigenporn berdasarkan ruang warna YCbCr. Berdasarkan pengujian yang dilakukan dengan 150 data uji dengan pembagian 2 type citra yaitu citra pornografi dan citra non pornografi, diperoleh akurasi sebesar 80.5%. Hasil eksperimen menunjukkan bahwa metode yang dilakukan berkinerja baik pada berbagai data pengujian, khususnya pada gambar yang berhubungan dengan manusia.

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“…Several algorithms for skin detection have been developed over the last years [10,11]. These may be divided into two major groups: pixel-based methods [12][13][14] and region-based methods [15][16][17][18]. While the former classifies each pixel as skin or non-skin without considering its neighborhood, the latter take advantage of pixels neighborhood to improve the color segmentation process.…”

Section: Introductionmentioning

confidence: 99%

Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation

Soltani¹,

Benabdelkader²

2021

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The human color skin image database called SFA, specifically designed to assist research in the area of face recognition, constitutes a very important means particularly for the challenging task of skin detection. It has showed high performances comparing to other existing databases. SFA database provides multiple skin and non-skin samples, which in various combinations with each other allow creating new samples that could be useful and more effective. This particular aspect will be investigated, in the present paper, by creating four new representative skin samples according to the four rules of minimum, maximum, mean and median. The obtained samples will be exploited for the purpose of skin segmentation on the basis of the well-known Euclidean and Manhattan distance metrics. Thereafter, performances of the new representative skin samples versus performances of those skin samples, originally provided by SFA, will be illustrated. Simulation results in both SFA and UTD (University of Texas at Dallas) color face databases indicate that detection rates higher than 92% can be achieved with either measure.

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Image Segmentation for Skin Detection

Cited by 7 publications

References 5 publications

Human Skin Detection and Segmentation Based on Convolutional Neural Networks

Human Skin Detection and Segmentation Based on Convolutional Neural Networks

DETEKSI CITRA PORNOGRAFI MENGGUNAKAN SKIN COLOR DETECTION DAN EIGENPORN BERDASARKAN RUANG WARNA YCbCr

Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation

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