Mitigating Gender Bias in Machine Learning Data Sets

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.i

Enhancing Automated Face Recognition with Makeup Detection: A CNN-Based Approach

This study delves into the complex issue posed by facial makeup, which has the potential to significantly alter the appearance of individuals, posing a challenge to automated face recognition systems, as well as age and beauty estimation methods. A model solution aimed at automatically detecting makeup in facial images to improve the accuracy of recognition systems was proposed in this work. The approach revolves around utilizing a sophisticated model that harnesses a feature vector encapsulating crucial aspects of facial attributes including shape, texture, and color. Employing an advanced Convolutional Neural Network (CNN) architecture, the model detects the presence of makeup by analyzing key facial landmarks such as eye distance, nose width, eye socket depth, cheekbones, jawline, and chin. Experiments were performed on a dataset consisting of 200 facial images to assess the effectiveness of the proposed method. The model achieved a validation accuracy of 100%, demonstrating its robustness in makeup face detection. Notably, the computational runtime for the validation process was 1 minute and 40 seconds, underscoring the efficiency of the proposed approach. Moreover, an innovative adaptive pre-processing strategy that capitalizes on makeup information to enhance the performance of facial recognition systems was developed. This strategy aims to optimize the recognition process by leveraging insights gained from makeup detection. By integrating this adaptive pre-processing step, further advancements in the accuracy and reliability of facial recognition technology, particularly in scenarios where makeup may confound traditional recognition methods, are envisioned

Integrated Multi-Model Face Shape and Eye Attributes Identification for Hair Style and Eyelashes Recommendation

Identifying human face shape and eye attributes is the first and most vital process before applying for the right hairstyle and eyelashes extension. The aim of this research work includes the development of a decision support program to constitute an aid system that analyses eye and face features automatically based on the image taken from a user. The system suggests a suitable recommendation of eyelashes type and hairstyle based on the automatic reported users’ eye and face features. To achieve the aim, we develop a multi-model system comprising three separate models; each model targeted a different task, including; face shape classification, eye attribute identification and gender detection model. Face shape classification system has been designed based on the development of a hybrid framework of handcrafting and learned feature. Eye attributes have been identified by exploiting the geometrical eye measurements using the detected eye landmarks. Gender identification system has been realised and designed by implementing a deep learning-based approach. The outputs of three developed models are merged to design a decision support system for haircut and eyelash extension recommendation. The obtained detection results demonstrate that the proposed method effectively identifies the face shape and eye attributes. Developing such computer-aided systems is suitable and beneficial for the user and would be beneficial to the beauty industrial.</jats:p

Artificial Intelligence Applied to Facial Image Analysis and Feature Measurement

Beauty has always played an essential part in society, influencing both everyday human interactions and more significant aspects such as mate selection. The continued and expanding use of beauty products by women and, increasingly, men worldwide has prompted and motivated several companies to develop platforms that effectively integrate into the beauty and cosmetics sector. They attempt to improve the customer experience by combining data with personalisation. Global cosmetics spending is worth billions of dollars, and most of it is wasted on unsuitable or incompatible products. This enables artificial intelligence to alter the rules using computer vision and deep learning approaches, allowing customers to be completely satisfied. With the advanced feature extraction in deep learning, especially convolutional neural networks, automatic facial feature analysis from images for the sake of beauty and beautification has become an emerging subject of study. Scholars studying facial aesthetics have recently made breakthroughs in the areas of facial shape beautification and beauty prediction. In the cosmetics sector, a new line of recommendation system research has arisen. Users benefit from recommendation systems since these systems help them narrow down their options. This thesis has laid the groundwork for a recommendation system related to beautification purposes through hairstyle and eyelashes leveraging artificial intelligence techniques. One of the most potent descriptors for attribution of personality is facial attributes. Various types of facial attributes are extracted in this thesis, including geometrical, automatic and hand-crafted features. The extracted attributes provide rich information for the recommendation system to produce the final outcome. The coexistence of external effects on the faces, like makeup or retouching, could disguise facial features. This might result in degradation in the performance of facial feature extraction and subsequently in the recommendation system. Thus, three methods are further developed to detect the faces wearing the makeup before passing the images into the recommendation system. This would help to provide more reliable and accurate feature extraction and suggest more suitable recommendation results. This thesis also presents a method for segmenting the facial region with the goal of extending the developed recommendation system by incorporating a synthesised hairstyle virtually on the facial region, thereby harnessing the recommended hairstyle generated by our developed system. Hence, the work presented in this thesis shows the benefits of implementing computational intelligence methods in the beauty and cosmetics sector. It also demonstrates that computational intelligence techniques have redefined the notion of beauty and how the consumer communicates with these emerging intelligent facilities that bring solutions to our fingertips

Penerapan Metode Certainty Factor Dalam Perancangan Aplikasi Diangnosa Penyakit Kulit Dengan Jenis Kosmetik

Skin is the outermost organ of the body that covers the human body. The skin makes up 15% of the total body weight. On the outer surface of the skin there are pores (cavities) where sweat is released. The skin has many functions, including as body armor, as a sense of touch or communication tool, and as a temperature regulator. Most people, especially women, have white, healthy, clean and well- groomed facial skin. Cosmetics Distributor is a line of business that sells cosmetic products. In identifying the consumer's facial skin type, it is carried out by employees who are not experts. Often there is an error asking the type of skin and to hire a doctor or expert requires a large amount of money. The problems in using products that do not pay attention to skin type or do not know it, causing new problems such as acne, dry skin and others. These problems can be solved by the field of science in detecting a person's facial skin based on expert knowledge, then the science is an Expert System using Certainty factor. The results of this study can identify the type of facial skin based on the existing symptoms, it is hoped that it can help distributors and detect skin quickly and accurately

Beauty is Beneficial: An Examination of Candidate Facial Attractiveness, Gender, Qualification, and Customer Visibility on Online Recruitment Intentions

The present study examined the effects of information included in candidates’ online networking profiles on recruiters’ perceptions and ratings of their likelihood of inviting the candidate for a job interview. Specifically, this study used a status generalization theory perspective to examine the weighting of information related to candidate physical attractiveness, gender, and qualification to predict perceived expectations for intellectual competence, likability, and social skills. These expectations then predicted whether the candidate should be recommended for a job interview. While participants relied almost exclusively on qualification information when making judgments of intellectual competence, candidates placed increased weight on attractiveness when rating likability and social skills. Using a unique policy-capturing HLM framework, these relationships were examined within high- and low-customer visibility positions and within both masculine- and feminine-typed jobs. The degree of in-person versus face-to-face customer contact required for the position did not affect participants’ reliance on attractiveness, and participants did not exhibit gender bias even when the position was described as stereotypically masculine or stereotypically feminine. Finally, this study examined the moderating effects of implicit and explicit attractiveness attitudes on expectations and found that more biased explicit, but not implicit, attitudes strengthened the degree to which participants relied on attractiveness information in making recruitment decisions. Because physical attractiveness discrimination is not directly covered under current employment law, it is important to examine attractiveness biases in organizational contexts to determine if recruitment and selection methods are functioning at the highest degree of validity possible. This has particular implications for training interventions that can be implemented to both reduce attractiveness biases and to increase the validity and fairness of selection systems