Deteksi Lokasi Bibir Otomatis Pada Citra Wajah Berbasis Ciri Bentuk dan Warna

Metode yang diusulkan merupakan gabungan antara pendekatan warna  dan bentuk bibir untuk deteksi otomatis lokasi bibir pada citra wajah yang gunakan untuk mengidentifikasi wajah seseorang berdasarkan fitur bibirnya. Penelitian ini mencoba untuk menggabungkan pendekatan berbasis ruang warna yang diperbaiki menggunakan pendekatan berbasis bentuk bibir. Hasil eksperimen menunjukkan bahwa 61,4% akurasi identifikasi ketika diuji menggunakan 500 citra wajah. Nilai Precision dan Recall digunakan untuk mengevaluasi teknik yang diusulkan dibandingkan dengan gambar yang disegmentasi secara manual yang selanjutnya diproses dalam sistem identifikasi wajah. Hasil ujicoba yang telah dilakukan dapat digunakan sebagai dasar pengembangan system identifikasi waktu nyata. Kata Kunci: Bibir Deteksi, Color Space, Identifikasi Waja

Robust Visual Lips Feature Extraction Method for Improved Visual Speech Recognition System

Recently, automatic lips reading ALR acquired a significant interest among many researchers due to its adoption in many applications. One such application is in speech recognition system in noisy environment, where visual cue that contain some integral information added to the audio signal, as well as the way that person merges audio-visual stimulus to identify utterance. The unsolved part of this problem is the utterance classification using only the visual cues without the availability of acoustic signal of the talker's speech. By taking into considerations a set of frames from recorded video for a person uttering a word; a robust image processing technique is used to isolate the lips region, then suitable features are extracted that represent the mouth shape variation during speech. These features are used by the classification stage to identify the uttered word. This paper is solve this problem by introducing a new segmentation technique to isolate the lips region together with a set of visual features base on the extracted lips boundary which able to perform lips reading with significant result. A special laboratory is designed to collect the utterance of twenty six English letters from a multiple speakers which are adopted in this paper (UOTEletters corpus). Moreover; two type of classifier (using Numeral Virtual generalization (NVG) RAM and K nearest neighborhood KNN) where adopted to identify the talker’s utterance. The recognition performance for the input visual utterance when using NVG RAM is 94.679%, which is utilized for the first time in this work. While; 92.628% when KNN is utilize

Extracción y clasificación de posturas labiales en niños entre 5 y 10 años de la ciudad de manizales

Se presentan los diferentes algoritmos y procedimientos utilizados en el desarrollo de un sistema de extracción y clasificación automática de posturas labiales. El sistema se diseñó con el fin de acompañar a los niños con labio y/o paladar hendido corregido, en el proceso de recuperación. Para la detección facial, se trabajan técnicas basadas en el espacio de color YCbCr y análisis de conectividad. La detección del contorno de los labios se realiza mediante técnicas de proyecciones, análisis de color (espacio de color HSV y Exclusión de Rojo) y la información de bordes del operador SUSAN. La extracción de la información discriminante se hace por diferentes tipos de análisis estadístico a partir de la región descrita por el contorno. La clasificación de las posturas se realiza empleando diferentes tipos de clasificadores

Visual Speech Recognition with Lightweight Psychologically Motivated Gabor Features

Extraction of relevant lip features is of continuing interest in the visual speech domain. 1 Using end-to-end feature extraction can produce good results, but at the cost of the results being 2 difficult for humans to comprehend and relate to. We present a new, lightweight feature extraction 3 approach, motivated by human-centric glimpse based psychological research into facial barcodes, 4 and demonstrate that these simple, easy to extract 3D geometric features (produced using Gabor 5 based image patches), can successfully be used for speech recognition with LSTM based machine 6 learning. This approach can successfully extract low dimensionality lip parameters with a minimum 7 of processing. One key difference between using these Gabor-based features and using other features 8 such as traditional DCT, or the current fashion for CNN features is that these are human-centric 9 features that can be visualised and analysed by humans. This means that it is easier to explain and 10 visualise the results. They can also be used for reliable speech recognition, as demonstrated using the 11 Grid corpus. Results for overlapping speakers using our lightweight system gave a recognition rate 12 of over 82%, which compares well to less explainable features in the literature. 1

Lip Features Automatic Extraction

International audienceAn algorithm for speaker's lip segmentation and features extraction is presented in this paper. A color video sequence of speaker's face is acquired, under natural lighting conditions and without any particular make-up. First, a logarithmic color transform is performed from RGB to HI (hue, intensity) color space. Second, a statistical approach using Markov random field modeling determines red hue prevailing region and motion in a spatiotemporal neighborhood. Third, the final label field is used to extract ROI (Region Of Interest) and geometrical features