Anthropometric Measurement of North-East Indian Faces for Forensic Face Analysis

Saha, Kumarjit; Bhowmik, Mrinal Kanti; Bhattacharjee, Debotosh

doi:10.1007/978-3-319-05885-6_7

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…[8,23] Few authors have studied the role of computed tomography scans, magnetic resonance scans and digital photography in craniofacial anthropometry. [5,24,25] The inherent risk of exposure to radiation, cost and availability are limitations to such types of anthropometry.…”

Section: Discussionmentioning

confidence: 99%

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Exploring Craniofacial Anthropometry in the Ethnic Adult Population of Meghalaya, Northeast India: A Cross-Sectional Study

Sinha,

Chakraborty,

Balakrishnan

et al. 2024

Preprint

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Craniofacial anthropometry is important for identity, diagnosis of congenital anomalies etc. The incidence of craniofacial abnormalities is high in the Southeast Asian region. The population of Meghalaya differs in features from other states of Northeast India and the neighbouring East Asia. The objective was to study the craniofacial anthropometric features of the ethnic adults in the Meghalaya state of Northeast India. This cross-sectional study was conducted from 2022 to 2023. Head length (HL), head breadth (HB), Cephalic index (CI), intercanthal distance (ICD), lateral canthal distance (LCD) and ear length (EL) were measured. A total of 104 adults were included. The majority of the individuals belonged to the Khasi community 51 (49%) followed by the Garo community 27 (26%) and the Jaintia community 26 (25%). The mean ± S.D age of the total sample population was 29.5 ± 8.4 years. The mean CI of the total sample population was 79.13 (mesocephalic). No statistically significant difference was noted between male-female CI in the total and subpopulation groups. In our study, the common head types were dolichocephalic (29%), brachycephalic (29%), mesocephalic (21%), hyperbrachycephalic (16%) and ultrabrachycephalic (5%). A statistically significant difference was noted between Khasi and Jaintia in HL, HW, CI, LCD; between Khasi and Garo in HL, ICD, LCD and between Garo and Jaintia in HW, CI, ICD. We believe our conventional craniofacial anthropometry data could add to the pool of regional anthropometry data and be used for future scientific purposes like anthropology, maxillofacial surgeries, designing helmets, forensic analysis and gender differentiation.

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Section: Discussionmentioning

confidence: 99%

“…The majority of the Northeast Indian population has Mongoloid features and hence hyperbrachycephalic features are expected. [5] Objective:…”

Section: Introductionmentioning

confidence: 99%

Exploring Craniofacial Anthropometry in the Ethnic Adult Population of Meghalaya, Northeast India: A Cross-Sectional Study

Sinha,

Chakraborty,

Balakrishnan

et al. 2024

Preprint

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“…Saha, Bhattacharjee and Bhowmik [34], in their paper, studied the facial structural differences between the various tribes and non-tribes of the north-east India. The aim of their study was to carry out a comparison of anthropometric values of Mongolians in the north-eastern region of India of similar socioeconomic status with special reference to five states: Assam, Mizoram, Tripura, Nagaland and Manipur [34]. They took facial images of mongoloid people from different states, e.g., Tripura, Reang, Chakma, Debbarma, Jamatia, Darlong, Mog, Halam etc.…”

Section: Literature Surveymentioning

confidence: 99%

Racial Classification from Face

Ahmed*¹,

Choudhury²,

Borbora³

2019

IJRTE

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Abstract: FACE is one of the major sources of social information like race, age, gender etc. At different levels of classification, prediction and identification face plays a major role, apart from other parts of the human body. As per literature Race is a form of classification for categorizing human beings in to groups based on geographic boundaries, physical appearances(including face), ethnicity and social status. In this paper we are trying to focus on different facial datasets those are currently available without any cost (but with licensing restrictions). Here, we are also representing our study of different works carried-out related with the racial classification and related topics.FERET. The two experiments were carried-out using ANN and CNN respectively. In the 1 st experiment, they (i) calculated Geometric Features, (ii) extracted Skin Color and (iii) calculated Normalized Forehead Area. For the network, they used, out of 447 images (from FERET), 320 were used for training, 37 for validation and 97 for testing. In the 2 nd experiment, they used a pre-trained model [37] for the extraction of features from the samples (both training and testing). The used Network consisted of 13 convolution layers. After performing the feature extraction, training and testing was performed. The result of the experiments showed superiority of CNN over ANN as the accuracies were 98.6% and 82.4% respectively irrespective of costs. It also showed that time taken for extraction of features and training of the network by CNN is more than ANN. VI. CONCLUSIONWe divided our survey in two parts: (i) about the different recognized facial datasets and the (ii) about different procedures and methods adopted by various researchers from feature selection to race and ethnicity classification. From the survey, it is noticed that the face can precisely give the race. The survey shows that in the last few years, lots of researches are carried out and which basically signifies a tremendous progress in the learning process of race from facial images. In this survey we tried to provides, more or less, all the existing facial image datasets and also a comprehensive review of the advances in race.

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“…Based on the result, defects under surface skin layers had been located by the surgeon that can limit the minimum damage and do not leave the scar after surgery. Facial anthropometric analysis [ 23 ] with computer aid is almost applied in many fields might be mentioned as diagnosing medical images, surgical planning [ 24 ], facial reconstruction surgeries, facial security [ 25 ], and also forensic investigation [ 26 ]. CNNs used an indirect anthropometry technique on approach research to provide the best digital image diagnoses where conventional calculation is restricted due to the need to measure on human skin using a purely manual measuring instrument, and since human skin is a soft surface, the ruler sinks into the skin, leading error to be recorded.…”

Section: Introductionmentioning

confidence: 99%

Shape Prediction of Nasal Bones by Digital 2D-Photogrammetry of the Nose Based on Convolution and Back-Propagation Neural Network

Tuan

Hai

Thinh

2022

Computational and Mathematical Methods in Medicine

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In rhinoplasty, it is necessary to consider the correlation between the anthropometric indicators of the nasal bone, so that it prevents surgical complications and enhances the patient’s satisfaction. The penetrating form of high-energy electromagnetic radiation is highly impacted on human health, which has often raised concerns of alternative method for facial analysis. The critical stage to assess nasal morphology is the nasal analysis on its anthropology that is highly reliant on the understanding of the structural features of the nasal radix. For example, the shape and size of nasal bone features, skin thickness, and also body factors aggregated from different facial anthropology values. In medical diagnosis, however, the morphology of the nasal bone is determined manually and significantly relies on the clinician’s expertise. Furthermore, the evaluation anthropological keypoint of the nasal bone is nonrepeatable and laborious, also finding widely differ and intralaboratory variability in the results because of facial soft tissue and equipment defects. In order to overcome these problems, we propose specialized convolutional neural network (CNN) architecture to accurately predict nasal measurement based on digital 2D photogrammetry. To boost performance and efficacy, it is deliberately constructed with many layers and different filter sizes, with less filters and optimizing parameters. Through its result, the back-propagation neural network (BPNN) indicated the correlation between differences in human body factors mentioned are height, weight known as body mass index (BMI), age, gender, and the nasal bone dimension of the participant. With full of parameters could the nasal morphology be diagnostic continuously. The model’s performance is evaluated on various newest architecture models such as DenseNet, ConvNet, Inception, VGG, and MobileNet. Experiments were directly conducted on different facials. The results show the proposed architecture worked well in terms of nasal properties achieved which utilize four statistical criteria named mean average precision (mAP), mean absolute error (MAE), R -square ( R 2 ), and T -test analyzed. Data has also shown that the nasal shape of Southeast Asians, especially Vietnamese, could be divided into different types in two perspective views. From cadavers for bony datasets, nasal bones can be classified into 2 morphological types in the lateral view which “V” shape was presented by 78.8% and the remains were “S” shape evaluated based on Lazovic (2015). With 2 angular dimension averages are 136.41 ± 7.99 and 104.25 ± 5.95 represented by the nasofrontal angle (g-n-prn) and the nasomental angle (n-prn-sn), respectively. For frontal view, classified by Hwang, Tae-Sun, et al. (2005), nasal morphology of Vietnamese participants could be divided into three types: type A was present in 57.6% and type B was present in 30.3% of the noses. In particular, types C, D, and E were not a common form of Vietnamese which includes the remaining number of participants. In conclusion, the proposed model performed the potential hybrid of CNN and BPNN with its application to give expected accuracy in terms of keypoint localization and nasal morphology regression. Nasal analysis can replace MRI imaging diagnostics that are reflected by the risk to human body.

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Anthropometric Measurement of North-East Indian Faces for Forensic Face Analysis

Cited by 4 publications

References 12 publications

Exploring Craniofacial Anthropometry in the Ethnic Adult Population of Meghalaya, Northeast India: A Cross-Sectional Study

Exploring Craniofacial Anthropometry in the Ethnic Adult Population of Meghalaya, Northeast India: A Cross-Sectional Study

Racial Classification from Face

Shape Prediction of Nasal Bones by Digital 2D-Photogrammetry of the Nose Based on Convolution and Back-Propagation Neural Network

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